A method for digital image stabilization (DIS) on a sequence of video frames captured by an image capture device comprises: obtaining motion values sampled from a motion signal and position values sampled from a position signal while capturing the frames, each frame being associated with a respective motion value and position value; computing, for each frame, a residual motion value to form a sequence of residual motion values; and performing DIS by (i) detecting an image feature in a reference frame, (ii) tracking the feature across subsequent frames, (iii) determining frame motion data from the feature's displacement in the subsequent frames, and (iv) generating a stabilized frame sequence based on the frame motion data. The size of a tracking window used to track the feature in each frame is set based on the residual motion values, thereby adapting the tracking to expected inter-frame motion to improve robustness and stabilization quality.
A computer-implemented method for tampering detection of a microphone device, the microphone device comprising a microphone arranged in a cavity open to surroundings of the microphone device via a microphone hole, the method comprising: receiving an audio signal from the microphone; sampling the audio signal for a low frequency band; detecting an event comprising an exponentially decreasing absolute amplitude of the sampled audio signal during a time period of at least one second; and when detecting the event, outputting a tampering detection signal.
A method for controlling an OIS system of an image capturing device, the method comprising: obtaining a first and a second time-series of samples derived from a motion signal of a motion sensor of the image capturing device, wherein the motion signal indicates a motion of the image capturing device, and wherein the first time-series spans a longer time period of the motion signal than the second time-series; determining a current dominant frequency for the motion signal based on a frequency analysis of the first time-series; and determining a setpoint for the OIS system by: fitting to the second time-series a model function based on a sine and/or cosine function and the current dominant frequency, extrapolating the fitted model function past a last sample of the second time-series to predict a successive sample, and determining the setpoint based on the predicted successive sample.
In some implementations, a device may encode a series of images of video data into an encoded video stream including key frames and delta frames. The delta frames may be arranged according to a hierarchical prediction pattern with multiple temporal layers and a predetermined structure. The device may obtain an instruction to encode a delta frame as a skip frame. The device may determine that the skip frame is to be encoded in a first temporal layer, of the multiple temporal layers, according to the predetermined structure. The device may evaluate whether an immediately preceding frame to the skip frame is encoded, or to be encoded, in a second temporal layer, of the multiple temporal layers, that is finer than the first temporal layer. The device may perform an action based on the evaluation.
H04N 19/132 - Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/177 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
H04N 19/31 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability in the temporal domain
5.
METHOD, DEVICE AND SYSTEM FOR TRACKING AN OBJECT IN A SEQUENCE OF VIDEO FRAMES
There is provided a method, device, and system for tracking an object in a sequence of video frames by using a tracking filter which is operable in two modes. In the first mode it estimates a state vector describing a position and velocity of the object in the video frames. In the second mode it estimates a state vector describing position, velocity, and acceleration of the object in the video frames. The method includes switching from the first mode to the second mode. The switching is performed in response to a velocity difference between a velocity of the object estimated by the tracking filter and a velocity of the object indicated by motion vectors from a video encoder and being larger than a threshold.
A device, and method as implemented by the device, configure a visual appearance of output images from a camera capturing a scene. The device is configured to obtain a first image depicting the scene captured by the camera; obtain a second image depicting the scene as modified by a user using an image manipulation software; extract a set of one or more visual differences between the first and second images, and determine, based on the extracted set of visual differences, one or more postprocessing operations, such that a third image depicting the scene captured by the camera and subjected to the one or more image postprocessing operations is more visually equal (or similar) to the second image than the first image. A corresponding system, computer program and computer program product are also provided.
Methods, software, devices and systems for video scrubbing enable a client device to retrieve images for scrubbing based on a user-requested time along a video timeline of a video stored in a server. The client device checks if a cached image meets specified conditions, including a timestamp within a precision margin around the requested time. The precision margin scales with the timeline length, providing a smaller margin for shorter timelines and a larger margin for longer timelines. If a relevant cached image is found, it is retrieved; if not, an image with a highest relevance score within the precision margin is fetched from the server and stored in memory.
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
H04N 21/433 - Content storage operation, e.g. storage operation in response to a pause request or caching operations
H04N 21/437 - Interfacing the upstream path of the transmission network, e.g. for transmitting client requests to a VOD server
8.
ADAPTIVE NEURAL NETWORK SELECTION METHOD AND SYSTEM
A computer-implemented method for segmentation and object detection in images captured by a camera having zoom capability comprising: obtaining images from the camera and obtaining a zoom factor, the zoom factor representing a current zoom level applied by the camera when capturing the images; selecting, based on the zoom factor, one neural network from a plurality of neural networks configured to segment the images and detect objects in the images. The plurality of neural networks operate at different image resolutions, wherein a higher zoom factor corresponds to selecting a neural network operating at a lower image resolution, and a lower zoom factor corresponds to selecting a neural network operating at a higher image resolution; down-scaling the images to the image resolution required by the selected neural network; applying the selected neural network to segment the down-scaled images and detect the objects in the down-scaled images.
A method for streaming live coded video to one or more clients comprises: detecting a plurality of video stream requests; obtaining maximum bitrate values that return video streams are allowed to have; calculating requested quality of experience, QoE, values; determining a number of return video streams being lower than the plurality of video stream requests, and having bitrates such that each of the video stream requests can be matched with a return video stream, wherein the bitrates of the return video streams are determined such that a cumulative difference between the QoE value of each video stream request and a calculated return QoE of a matching return video stream is minimized; obtaining the return video streams; and streaming the return video streams. A streaming controller for controlling streaming of live coded video is also disclosed.
H04N 21/2662 - Controlling the complexity of the video stream, e.g. by scaling the resolution or bitrate of the video stream based on the client capabilities
A method for controlling an image stabilization (IS) system of an image capturing device. The IS system comprises an optical image stabilization (OIS) system that compensates for vibrational movement based on a motion signal from the image-capturing device's motion sensor. The OIS system has a first control parameter with an adjustable value to adapt responsiveness to vibrational movement, the value being adjustable between a lower responsiveness and a higher responsiveness of the OIS system. While the OIS system is in an active state and performs OIS, it determines a signal-to-noise estimate for the motion signal, and also determines the value of the first control parameter based on the signal-to-noise estimate, comprising setting the first control parameter to the first value responsive to determining that the signal-to-noise estimate is in a lower range, and to the second value responsive to determining that the signal-to-noise estimate is in an upper range.
A computer-implemented method to adjust a process noise of an object tracker in a camera is configured to monitor a scene including moving objects, the object tracker uses a motion model associated with process noise, the method comprising: determining, using the object tracker, object tracks of moving objects travelling along a path in the scene for a time period using a level of process noise, evaluating at least one spatial parameter of the determined object tracks in relation to a predetermined criterion, repeating the steps of determining object tracks and evaluating at least one spatial parameter for object tracks during subsequent time periods using increased process noise for each iteration until the predetermined criterion is fulfilled or a maximum process noise limit is reached, and storing data indicating the final process noise.
A method for intra refresh encoding of a video stream includes encoding the video stream using a first intra refresh pattern; determining that a time-variation of bits-per-frame for encoding the video stream using a second intra refresh pattern different from the first intra refresh pattern is lower than that for encoding the video stream using the first intra refresh pattern, and switching to encoding the video stream using the second intra refresh pattern. A corresponding device configured to perform the method is also provided, as well as a corresponding computer program and computer program product.
H04N 19/593 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
H04N 19/11 - Selection of coding mode or of prediction mode among a plurality of spatial predictive coding modes
H04N 19/184 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
13.
IDENTIFYING A POTENTIAL FALSE POSITIVE DETECTION BOX
Anchor-based object detection system and method that identify potential false positive detections among three detection boxes in a same image frame. Each detection box has a predicted IoU score representing confidence that it captures an object. The system determines overlap of a second box with a first and third box, where the second is positioned between them. It identifies the second box as a potential false positive if it determines the IoU score of the second box is below a set threshold and if corresponding reference points within each box is determined to be substantially aligned, with the second box's reference point close to an alignment line defined by the first and third boxes.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
A camera device comprising a first printed circuit board (PCB) segment provided with a first grounding contact surface, a second PCB segment provided with an opening defined by a portion of the second PCB segment forming a second grounding contact surface, a chassis made of an electrically conductive material, a clip made of an electrically conductive material and snap fitted to the chassis; wherein the chassis is configured to support an image sensor and the first and second PCB segment, wherein the first PCB segment is arranged between the chassis and the second PCB segment, wherein the clip comprises a first contact portion abutting the first grounding contact surface of the first PCB segment; and a second contact portion extending through the opening of the second PCB segment and engaging the second grounding contact surface thereof.
H04N 23/52 - Elements optimising image sensor operation, e.g. for electromagnetic interference [EMI] protection or temperature control by heat transfer or cooling elements
H04N 25/79 - Arrangements of circuitry being divided between different or multiple substrates, chips or circuit boards, e.g. stacked image sensors
15.
INDICATING CLOCK DRIFT OF AN INTERNAL CLOCK IN A SENSOR DEVICE
A method for indicating clock drift of an internal clock comprised in a sensor device comprises: capturing, by the sensor device, first data frame; associating, by the sensor device, the first data frame with a first point in time indicating a point in time of the internal clock when the first data frame being captured; receiving, by the sensor device via a network protocol for clock synchronization, time synchronisation data; determining, by the sensor device using the time synchronisation data, a reference point in time indicating a point in time of a reference clock when the first data frame being captured; determining, by the sensor device, an offset between the first point in time and the reference point in time; and upon the offset exceeding a threshold offset, associating, by the sensor device, the first data frame with data indicating the offset.
A method for determining occupied positions in a space starts by receiving detections of object movement from a radar monitoring the space. Each detection is associated with a position in the space and a time point when the detection was made. The method accumulates detections that have a time point within a first time period of predefined duration, and identifies a set of clusters of detections in the space by analyzing similarity in position of the accumulated detections. Any cluster whose detections are temporally distributed within a proportion of the first time period which is below a predefined proportion threshold are then removed from the set of clusters. The positions in the space that correspond to the set of clusters are determined to be occupied during the first time period.
A screw joint arrangement that allows a second member to be fixed or movable relative to a first member. The first member includes at least one screw-receiving opening. The second member includes a bore having, in order from the first member side, a first threaded section, a non-threaded section, and a second threaded section. A screw with a head and a shaft carrying a threaded portion cooperates with these features to provide two selectable states. In the fixed state, a screw-receiving opening is aligned with the bore and the screw's threaded portion engages the second threaded section, clamping the first member between the screw head and the second member. In the movable state, rotation advances the screw until its threaded portion lies within the non-threaded section while the shaft passes through the first threaded section, thereby releasing the clamp and retaining engagement between the members for adjustment.
Techniques update background layers during encoding of scene, performed by an image processing device. The scene is encoded based on classifying objects depicted in the scene as either foreground or background. The background is divided into ordered background layers where each background layer is associated with a respective depth model. The method comprises detecting) a change in an image portion of one background layer. The method comprises calculating a difference between the image portion and a corresponding image portion of a background layer ordered behind the one background layer. The method comprises selecting, when the difference is smaller than a threshold, the background layer ordered behind the one background layer to represent the image portion.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
An image-capturing system comprises: an image sensor configured to capture images of a scene at a periodic rate; an illumination source for illuminating the scene; measurement and control circuitry configured to: continuously measure the current to/from the image sensor; continuously detect periodically alternating high and low levels of the measured current; and control the illumination source to alternate between an on state and an off state with an illumination frequency equal to a switching frequency of the detected periodically alternating high and low levels of the measured current such that the illumination source is in the on state during exposure of the image sensor. The disclosure further relates to a method of controlling an illumination source for illuminating a scene during image sensor exposures.
A method and device estimate available transmission bandwidth of a network connection used to deliver a data file within a predetermined transmission time. A file size is obtained and a minimum average bitrate required to meet the time is computed as size divided by time. The transmission window is partitioned into alternating first and second time intervals. During each first interval, portions of the file are sent at a bitrate greater than the minimum average. During each second interval, remaining portions are sent at a lower bitrate selected such that the overall average bitrate across the window is at least the minimum average and below a configured bitrate threshold. Feedback (e.g., acknowledgments, loss, delay, or throughput) collected during the higher-rate first intervals is used to estimate the available bandwidth of the connection. The estimate may be used to adjust subsequent interval rates, file scheduling, or admission decisions.
A method, an apparatus and a system for estimating a ground surface model of a scene in which a camera and a radar are arranged. The method comprises receiving a current estimate of a ground surface model, receiving radar detections indicative of azimuth angle and distance in relation to the radar, receiving camera detections indicative of a direction in relation to the camera and representing the radar detections and the camera detections in a common coordinate system. The method further comprises identifying (a radar detection and a camera detection which match each other, determining a point in a global coordinate system which is at the distance in relation to the radar indicated by the identified radar detection and in the direction in relation to the camera indicated by the identified camera detection, and updating the current estimate of the ground surface model in view of the determined point.
A method, system and software for controlling a list of inactive tracks in an object tracking system. The techniques described includes obtaining the false positive rate, FPR, of a re-identification model using a decision threshold, and obtaining an acceptable FPR of the tracking system. The re-identification model is used to match new detections with inactive tracks. The techniques include determining one or more terminal conditions for deleting inactive tracks based on the acceptable and model FPRs. If a first inactive track meets a terminal condition, it is deleted from the list.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/98 - Detection or correction of errors, e.g. by rescanning the pattern or by human interventionEvaluation of the quality of the acquired patterns
Object detection is addressed through a structured method that emphasizes thresholding and signal regularization. Radar measurements of a scene provide intensity data distributed across points in either a range-Doppler or range-angle plane. A one-dimensional subset of the selected plane is parametrized to establish a simplified domain for analysis. From this subset, intensity values falling below a predefined clutter threshold are extracted, isolating potentially useful information from the clutter background. These extracted values are then regularized with respect to the parameter, producing a signal mask that captures consistent variations while suppressing noise fluctuations. A detection threshold is derived from the signal mask and subsequently applied to the radar data of the subset to determine the presence of objects. This approach enhances robustness in cluttered environments by dynamically adapting the detection threshold, improving the accuracy and reliability of radar-based object identification.
There is provided techniques for encoding video data composed of video frames. Each video frame is split into video frame portions. The video frame portions comprise blocks of video data. A method is performed by an image processing device implementing at least two video encoders. The method comprises encoding the blocks of video data using the video encoders with one video encoder per video frame portion. A quantization parameter to be used for encoding the blocks of video data that are at a border of any of the video frame portions that neighbours blocks of video data in another video frame portion is selected to a quantization parameter value that is lower than a default quantization parameter value for the blocks of video data.
H04N 19/196 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters
H04N 19/105 - Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
26.
METHOD FOR CONTROLLING AN IMAGE PROCESSING STAGE FOR PROCESSING IMAGE DATA CAPTURED BY A SURVEILLANCE CAMERA
A method for controlling an image-processing stage for image data captured by a downward-looking surveillance camera with a field of view greater than 180° includes obtaining image data comprising (i) a first set of pixels depicting a central scene portion below the horizon and (ii) a second set of pixels depicting a peripheral scene portion above the horizon. A ceiling-detection procedure analyzes pixels of the second set to determine whether the image-processing stage should operate in a ceiling operational mode. When the ceiling operational mode is selected, the image-processing stage is configured to process subsequently captured image data—again comprising first and second pixel sets—such that at least one image-processing operation is applied to the first set of pixels and not to the second set of pixels. Selectively disabling processing for peripheral pixels above the horizon reduces artifacts and computational load when a ceiling occupies the peripheral portion.
A method for identifying visually similar objects in image sets begins with receiving indications of regions where reference objects appear. From these regions, a reference pool is created, containing data records for each reference object. Neural networks are applied to extract both text embeddings (TEs) and visual embeddings (VEs) from the reference objects. Next, candidate objects are detected across the image set, with corresponding TEs and VEs generated for each. A candidate object is approved for inclusion in the reference pool only if at least one existing reference object meets a first similarity criterion (C1), which jointly considers TE similarity and VE similarity. Approved candidates are then added to the reference pool, expanding its coverage. Finally, a candidate object is identified as visually similar to the reference objects if the extended pool contains a reference object that satisfies a second similarity criterion (C2), based solely on VE similarity.
An apparatus and a method for tracking a temporarily occluded object in image frames of objects and associating detected instances into tracks. An active first track of a first object and an active second track of a second object are identified, which objects are moving together. Upon determination that the first track, but not the second track, has become inactive connecting it to the second track. Thereafter, a detected object instance is associated with an inactive track when a similarity score between the detected instance and the inactive track fulfills a similarity requirement. The requirement is less strict when the instance is spatially proximate to the second track and when the detected instance is compared to the inactive first track connected to the second track as compared to when the detected instance is compared to an unconnected inactive track.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/62 - Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extractionPattern tracking
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
A method for object detection (post-processing) in an image is provided, and includes obtaining, from one or more artificial neural network (ANN) entities trained to localize objects and one or more subparts of such objects in images, a plurality of object proposals and one or more subpart proposals in a same image; performing a first filtering of the object proposals; matching subpart proposals with corresponding object proposals remaining after the first filtering, and performing a second filtering of the unmatched object proposals remaining after the first filtering. The first and second filtering are based on classification confidence scores and proximity scores of the object proposals, and the second filtering is statistically more aggressive than the first filtering. A corresponding device, computer program and computer program product are also provided.
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
Security surveillance apparatus; video surveillance apparatus; electric and electronic video surveillance installations; apparatus and instruments for measuring, detecting and monitoring; intrusion detection systems; intrusion detection apparatus; apparatus and instruments for traffic monitoring and traffic surveillance; license plate recognition systems; cameras, including but not limited to digital cameras, network cameras, web cameras, IP cameras, thermal cameras; access control apparatus; access control installations; intercom apparatus; intercom installations; video intercom systems; audio intercom systems; accessories for video surveillance apparatus, access control apparatus, intercom apparatus, speakers and network audio systems; cloud servers; cloud computing software; software; software for surveillance, intercom and access control; software for video surveillance apparatus, access control apparatus, intercom apparatus, network audio systems, alarms, sensors and detectors; software for analysis of video, audio and sensor data; software for traffic monitoring and traffic surveillance, license plate recognition and traffic analysis; software for facial recognition; software (AI); artificial intelligence software for surveillance, intercom and access control; artificial intelligence software for video surveillance apparatus, access control apparatus, intercom apparatus, network audio systems, alarms, sensors and detectors; artificial intelligence software for analysis of video, audio and sensor data; artificial intelligence software for traffic monitoring and traffic surveillance, license plate recognition and traffic analysis; artificial intelligence software for facial recognition; camera housings; mounting devices for cameras; mounts for body worn cameras; casings and covers for cameras; camera lenses; control boards; hard drives; power accessories; joysticks, keypads, illuminators (other than for medical use), cables and connectors, installation displays all for the use together with cameras, video surveillance apparatus, access control apparatus, intercom apparatus, network audio systems, alarms and sensors; video encoders; video decoders; camera servers; chips (integrated circuit); motion control devices for cameras; optical motion sensing and tracking devices for cameras; radars; radar systems; lidar; lidar systems; drone detection systems; systems for detection, tracking and monitoring of UAV; sensors; detectors; air quality sensors; motion sensors; environmental sensors; people counters; microphones; speakers; network audio systems; display speakers; audio and visual alarms; encoded key cards; Card readers; none of the aforementioned goods in relation to management information system such as supplier relationship management, business intelligence, logistics, material management, business process reengineering and management; not to automatic data integration or exchange of business-process data; not to music production, musical composition, and/or production, or musical instruments; not to scales; not to accounting and financial information systems and software. Professional consulting activities in the form of professional technical expertise (non business); technical expertise; computer consulting services regarding program design for microprocessors; updating of computer programs for text, video, audio, images and data processing; consultation and research in the field of security systems, surveillance, intercom, access control, network audio systems, alarms, radars, sensors, detectors, computer hardware, software, computer infrastructure, computer processing, electronic data, video and camera techniques, image and audio processing, video and audio analytics, alarm analytics, and sensor and radar analytics; design and development of security systems, video surveillance apparatus, access control apparatus, intercom apparatus, network audio systems, alarms, radars, sensors, detectors, computer systems; design and development of video and camera techniques, image and audio processing, video and audio analytics, alarm analytics, and sensor and radar analytics; consultation in the field of product development; design, development and testing of new products; consultancy and research in the field of system integration; computer system analysis; design and development of software; maintenance, support and updating of software; computer programming; engineering; supply of technical know-how; industrial designing; cloud services; software as a service; software as a service featuring AI; software as a service featuring software for surveillance, intercom and access control; software as a service featuring software for video surveillance apparatus, access control apparatus, intercom apparatus, network audio systems, alarms, sensors and detectors; software as a service featuring software for analysis of video, audio and sensor data; software as a service featuring software for traffic monitoring and traffic surveillance, license plate recognition and traffic analysis; software as a service featuring software for facial recognition; none of the aforementioned services in relation to management information systems such as supplier relationship management, business intelligence, logistics, material management, business process reengineering and management; not to automatic data integration or exchange of business-process data; not to music production, musical composition, and/or production, or musical instruments; not to scales; not to accounting and financial information systems and software. Services concerning burglar alarms, security and surveillance, security alarms, security systems, surveillance systems, radars, sensors, detectors, access control and intercom systems; monitoring of burglar and security alarms; security services for the protection of property and individuals by means of electronic surveillance, burglar alarms, security alarms, security systems, surveillance systems, radars, sensors, detectors, access control and intercom systems; computerized surveillance services relating to break and enter; licensing of software; licensing of know-how; licensing of intellectual property; none of the aforementioned services in relation to management information systems such as supplier relationship management, business intelligence, logistics, material management, business process reengineering and management; not to automatic data integration or exchange of business-process data; not to music production, musical composition, and/or production, or musical instruments; not to scales; not to accounting and financial information systems and software.
34.
METHOD, DEVICE, SYSTEM, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR RENDERING OVERLAYS IN A HIERARCHICALLY ENCODED VIDEO SEQUENCE
Rendering overlays in a hierarchically encoded video sequence having an enhancement layer and a base layer is embodied in one or more methods, devices, systems and software. A video sequence is represented at a first resolution and a second resolution. A first overlay comprising a first pattern of glyphs is rendered in the video sequence at the first resolution, wherein a first glyph of the one or more glyphs is rendered at a first pixel in the video sequence at the first resolution. A second overlay comprising the first pattern is rendered in the video sequence at the second resolution. The rendering of the second overlay is controlled to render the first glyph at a second pixel position obtained by mapping the first pixel position to the second pixel position according to a ratio between the first resolution and the second resolution.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 19/33 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability in the spatial domain
The present disclosure relates to a mountable camera device, comprising: an image capturing unit for capturing a sequence of images, the image capturing unit comprising at least a lens and an image sensor; a vibration sensor for measuring camera vibrations; an optical image stabilization mechanism for physically compensating for the camera vibrations by physically moving the lens and/or the image sensor; electronic image stabilization circuitry for digitally compensating for the camera vibrations by image processing; and processing circuitry configured to select between the optical image stabilization mechanism and the electronic image stabilization circuitry by comparing the camera vibrations against a predefined stabilization margin and/or based on a location of an object of interest in the sequence of images. The disclosure further relates to a method for controlling a mountable camera device having optical image stabilization capabilities and electronic image stabilization capabilities.
H04N 23/68 - Control of cameras or camera modules for stable pick-up of the scene, e.g. compensating for camera body vibrations
H04N 23/61 - Control of cameras or camera modules based on recognised objects
H04N 23/695 - Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
H04N 23/741 - Circuitry for compensating brightness variation in the scene by increasing the dynamic range of the image compared to the dynamic range of the electronic image sensors
A solution for analyzing images of a scene captured under different visibility conditions includes obtaining images of a scene captured by one or more cameras and, for each image, obtaining an indication of an actual or assumed visibility (distance) at the scene when the image was captured; selecting, based on the visibility, an artificial neural network (ANN) architecture from a plurality of ANN architectures, wherein the ANN architectures are each trained for image analysis but configured for different input image resolutions, and analyzing the image using the selected ANN architecture. If the selected ANN architecture has a lower input image resolution than the image, the image may be downscaled to match the input image resolution of the selected ANN architecture.
A method and processing device for providing a scene segmentation map for a total field of view of an image capturing device comprises obtaining a scene segmentation map for the total field of view; receiving an indication that a new focus value has been set for the image capturing device for acquiring images of a current field of view; comparing the new focus value with a stored focus value associated with a focus region in the current field of view, wherein the stored focus value represents a previously used focus value; upon the new focus value deviating from the stored focus value more than a trigger threshold, triggering a scene segmentation map update process; and otherwise maintaining the scene segmentation map.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
The present system and method generally relate to the field of camera surveillance, and in particular to verifying image data in image data frames captured by a rolling shutter image sensor of a camera.
This disclosure relates to techniques described for optimizing real-time video streaming by dynamically managing data transmission and bandwidth estimation using padding data as a buffer to detect network congestion early.
A method determines swaying objects for a plurality of range intervals based on range-Doppler maps provided by a radar system. Each range-Doppler map corresponds to a time interval of a sequence of time intervals and comprises a respective energy value for a plurality of velocity intervals for each range interval. A sequence of differences is calculated for the time intervals. For each time interval, a difference between a statistical measure of energy values for a set of velocity intervals with positive velocities for the range interval and the statistical measure of energy values for a set of velocity intervals with negative velocities for the range interval is calculated. A frequency spectrum is then determined. On condition that there is a peak in the frequency spectrum for a frequency above a frequency threshold, it is determined that there are one or more swaying objects at the range interval.
G01S 13/536 - Discriminating between fixed and moving objects or between objects moving at different speeds using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves
47.
METHOD AND APPARATUS FOR TRACKING AN OBJECT IN A SEQUENCE OF IMAGE FRAMES
A method for tracking an object in a sequence of image frames. A first tracker is used to determine a track of an object in a sequence of image frames by using a linear motion model associated with a process noise. A second tracker is used to determine a track of motion in the sequence of image frames. A spatial overlap in the image frames between the track of the object and the corresponding track of motion is monitored over time. The process noise used by the first tracker is adjusted to increase the uncertainty of the linear motion model as the spatial overlap decreases and decrease the uncertainty of the linear motion model as the spatial overlap increases.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
A method for attaching an image sensor to an optics unit of an imaging device, wherein an attachment part is releasably attached to a sensor attachment surface of the optics unit and the image sensor is releasably attached to a sensor holder. Using an active alignment method, a position of at least one of the image sensor and the optics unit is adjusted, such that the image sensor is positioned at a desired angle in relation to an optical axis of the optics unit and in a desired position in relation to the optics unit, thereby positioning the image sensor in an aligned position. The sensor holder is permanently attached to the attachment part, such that the image sensor is fixed in the aligned position. The image sensor is releasable from the sensor holder when the sensor holder has been permanently attached to the attachment part.
The present disclosure relates to a method implemented in a camera that augments objects in a video stream by switching between two modes controlled by Pan-Zoom-Tilt, PTZ, commands. Initially, the camera receives a signal to activate the second mode, focused on object augmentation rather than standard PTZ configurations. It then determined the spatial distance from the camera to each object using indicating a spatial coordinate of the object. Following this, a PTZ command is received to determine a zoom parameter, which helps determine a range of spatial distances. Objects within this range are selected and subsequently augmented within the video feed, enhancing the informational value of the video stream.
A microphone arrangement includes a microphone element, a housing, containing the microphone element and associated electronics, and fastening portion located at a bottom of the microphone arrangement, configured to attach the microphone arrangement to the structural surface. The housing has an audio opening facing a clearance defined between the housing and the structural surface, and wherein the clearance has a clearance exit in an outer lateral perimeter of the microphone arrangement. The arrangement is enables reduction of interference from sound reflections off of structural surfaces onto which the microphone arrangement is attached.
H04R 1/28 - Transducer mountings or enclosures designed for specific frequency responseTransducer enclosures modified by provision of mechanical or acoustic impedances, e.g. resonator, damping means
H04R 1/04 - Structural association of microphone with electric circuitry therefor
Encoding a sequence of frames in a video stream, comprises receiving the sequence of frames at a first frame rate, encoding every second frame in the received sequence in a first base layer employing intercoding and intracoding, inserting skip frames between the frames encoded in the first base layer, such that every second frame in the first base layer is intercoded with a reference to copy image content of a previous encoded frame in the first base layer, encoding remaining frames in the received sequence in a first Low Complexity Enhancement Video Coding (LCEVC) layer associated with the first base layer, employing residuals and references to corresponding skip frames in the first base layer, and embedding the first LCEVC layer in the first base layer to obtain a first sequence of encoded frames at the first frame rate.
H04N 19/105 - Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
H04N 19/107 - Selection of coding mode or of prediction mode between spatial and temporal predictive coding, e.g. picture refresh
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/187 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a scalable video layer
H04N 19/30 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability
H04N 19/46 - Embedding additional information in the video signal during the compression process
H04N 19/70 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
A camera, a non-transitory computer-readable storage medium, and a method for triggering a second camera in a camera system to perform one or more actions prior to an object appearing in the field of view (FOV) of the second camera are disclosed. The camera system includes a second camera for which location and FOV are known, and a first device for which location is known. The method comprises receiving, in the second camera, information indicating that the object is approaching the FOV. The received information is based on a location and a direction of movement of the object. In response to the received information, the second camera is triggered to perform the following actions prior to the object appearing in FOV of the second camera: generating an intra frame or initiating an intra refresh procedure, adjusting one or more image processing parameters, and adjusting an exposure time.
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
H04N 23/66 - Remote control of cameras or camera parts, e.g. by remote control devices
H04N 23/81 - Camera processing pipelinesComponents thereof for suppressing or minimising disturbance in the image signal generation
H04N 23/88 - Camera processing pipelinesComponents thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
Signing of an encoded video stream using a first device and second device, wherein the first device provides the original signatures of a first encoded video stream, wherein the second device applies video processing to the first encoded video stream to modify the image frames thereof and include the modified image frames into a second encoded video stream. The second device determines difference data corresponding to the video processing and includes the difference data in the second encoded video stream. On a decoder side, the decoder can authenticate the received second encoded video stream by validating the original signatures using the difference data.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04N 21/4405 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving video stream decryption
58.
SYSTEM AND METHOD FOR DAISY CHAIN ADDRESSING IN A SPEAKER SYSTEM
A speaker system comprises a main unit and passive units connected by control voltage signal, reference voltage signal and audio signal connections to form a daisy chain. The main unit generates a control voltage through the control voltage signal connection; a reference voltage through the reference voltage signal connection; and an audio signal through the audio signal connection. The system further comprises a voltage regulation unit between passive units on the reference voltage signal connection; each voltage regulation unit providing a local reference voltage to each passive unit, wherein the local reference voltages to the passive units gradually increase or decrease between subsequent passive units along the daisy chain. Each passive unit comprises: a speaker; a comparator unit for comparing the control voltage to first and second voltage thresholds from the local reference voltage to generate an audio enable signal enabling the speaker unit to play the audio signal.
A signed video bitstream obtained by prediction coding of a video sequence includes data units and associated signature units, wherein each data unit represents at most one macroblock in a video frame of the video sequence, and each signature unit includes a digital signature of a bitstring derived from fingerprints of exactly one associated data unit each. The bitstream is edited by: receiving a request to substitute a region; determining a first set of macroblocks, in which said region is contained, and a second set of macroblocks referring to macroblocks in the first set; adding an archive object that includes fingerprints of a first and a second set of data units, which represent the first and second set of macroblocks; editing the first set of data units in accordance with the request to substitute; and re-encoding the second set of data units.
A method for detecting objects in a scene comprises capturing video of the scene, segmenting a first image in into first and second scene areas whereby detection conditions are expected to differ between the scene areas. A first object detection threshold is set for the first scene area. A first object in the first image is detected, a first confidence value for the detection of the first object being above the first object detection threshold. In a plurality of images subsequent to the first image, the first object is tracked as it moves into the second scene area. A second confidence value for a detection of the first object in the second scene area is determined. A second object detection threshold for the second scene area is set such that the second confidence value is above the second object detection threshold.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
METHOD AND SYSTEM FOR COUPLING A FIRST DATA SEQUENCE AND A SECOND DATA SEQUENCE TO EACH OTHER, AND METHOD AND DEVICE FOR VALIDATING THE FIRST AND SECOND DATA SEQUENCES AS BEING COUPLED
A system and method for coupling temporally related data sequences to each other to enable validation of them as being coupled. A first device, processing a first sequence captured during a first time, generates a first digital signature based on first data of the first sequence and on a first secret number; incorporates the first secret number and the first digital signature in the first sequence; and transmits the first sequence. A second device, processing a second sequence captured during a second time partly overlapping with the first time, generates a second digital signature based on second data of the second sequence and on a first digest generated on the first secret number; incorporates the generated second digital signature and the first digest in the second sequence, whereby the first and second sequences are coupled by the first secret number and the first digest; and transmits the second sequence.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
A power supply system with built-in fault detection includes a transformer, wherein the primary side and the secondary side are galvanically isolated and wherein an input voltage is supplied to the primary side of the transformer; one or more switching elements connected to the primary side of the transformer; a controller configured to control switching of the one or more switching elements to control an output voltage on the secondary side; an isolation capacitor connected between the secondary side of the transformer and the primary side of the transformer; fault circuitry connected to the isolation capacitor and configured to detect a switching pattern propagated through a short circuit between the primary side and secondary side of the transformer and further propagated through the isolation capacitor. Similarly, an electrical apparatus includes the power supply system and a method of handling faults in a non-earthed electrical apparatus employs the power supply.
H02M 3/335 - Conversion of DC power input into DC power output with intermediate conversion into AC by static converters using discharge tubes with control electrode or semiconductor devices with control electrode to produce the intermediate AC using devices of a triode or a transistor type requiring continuous application of a control signal using semiconductor devices only
G01R 31/52 - Testing for short-circuits, leakage current or ground faults
H02M 1/32 - Means for protecting converters other than by automatic disconnection
H05K 7/14 - Mounting supporting structure in casing or on frame or rack
An AC-DC power converter includes: a rectification circuit configured to convert an input AC voltage to a rectified AC voltage; a power factor correction boost converter circuit configured to convert the rectified AC voltage to a first boosted DC voltage, wherein the first boosted DC voltage has AC ripple; a switched mode power supply output stage having a transformer; a buck and/or boost converter having two input voltage terminals, wherein a first voltage input terminal is connected to the first boosted DC voltage or to a secondary side of the transformer, and wherein a second input voltage terminal is connected to a second DC voltage, wherein the second DC voltage is higher than the voltage on the first voltage input terminal within a predefined voltage range, and wherein the buck and/or boost converter uses pulse width modulation to generate an output DC voltage with reduced AC ripple.
H02M 7/217 - Conversion of AC power input into DC power output without possibility of reversal by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only
H02M 1/14 - Arrangements for reducing ripples from DC input or output
H02M 1/42 - Circuits or arrangements for compensating for or adjusting power factor in converters or inverters
H02M 3/158 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using discharge tubes with control electrode or semiconductor devices with control electrode using devices of a triode or transistor type requiring continuous application of a control signal using semiconductor devices only with automatic control of output voltage or current, e.g. switching regulators including plural semiconductor devices as final control devices for a single load
68.
METHOD AND VIDEO PROCESSING SYSTEM FOR UPDATING A BUFFER
An image processing system stores a set of pixel values for a set of pixels relating to a sequence of video frames obtained from a buffer. The pixel values have been filtered using a filtering algorithm to reduce temporal noise. A measure noise related to the pixel values is obtained and a current set of pixel values is obtained from a sensor. A new set of pixel values is then determined based on the stored pixel values and the current pixel values using the filtering algorithm to reduce temporal noise. A measure of noise in the new set of pixel values is then determined and the new set of pixel values is quantized. The higher amount of noise in the new set of pixel values, the higher quantizing is performed. The quantized set of pixel values are compressed and the buffer is updated with the compressed quantized pixel values.
H04N 19/147 - Data rate or code amount at the encoder output according to rate distortion criteria
H04N 19/126 - Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
In some implementations, a client device may detect user input indicating a requested time along a timeline of a video, the video being stored at a server device. The client device may check if a cached image having a timestamp within a precision margin of the requested time is stored in a memory of the client device. Under a condition that the cached image is present in the memory, the client device may retrieve the cached image from the memory of the client device. Under a condition that the cached image is not present in the memory, the client device may retrieve a corresponding image from the server device. The client device may adjust a size of the precision margin to be proportional to a length of the timeline such that the size of the precision margin proportionally increases and decreases with the length of the timeline.
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
70.
LENS HOLDER, A SENSOR UNIT, AND AN IMAGE CAPTURING DEVICE
A lens holder for an optics unit of an image capturing device, the lens holder having a body extending in a lens holder plane and comprising: a lens mount arranged on the body configured to support a lens of the optics unit and extending along a longitudinal axis perpendicular to the lens holder plane; and support posts extending from the body of the lens holder along the longitudinal axis and supports a sensor unit aligned with the optics unit, wherein each support post has a first end adjoining the body of the lens holder, and a free second end, and wherein the first end of one or more support posts adjoins a respective displacement portion of the body of the lens holder, which displacement portion is configured to be displaced out of the lens holder plane in response to a thermally induced force acting on the support post.
A method for tracking objects in a scene, comprises detecting object candidates in an image frame, and calculating, for each object candidate, an association measure which is indicative of a likelihood that the object candidate is associated with a current object track. Additionally, a view of the overview camera is correlated with a heatmap of the scene, the heatmap providing data indicative of areas in the scene having an elevated degree of occurrence of historical verified object tracks, and adjusting the association measure or an association threshold for object candidates which according to the heatmap are located in areas in the scene having an elevated degree of occurrence of historical verified object tracks so as to increase their probability of being associated with a current object track. Each object candidate is then associated with a current object track if the association measure is above an association threshold.
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
H04N 23/61 - Control of cameras or camera modules based on recognised objects
H04N 23/69 - Control of means for changing angle of the field of view, e.g. optical zoom objectives or electronic zooming
H04N 23/695 - Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
72.
METHOD, APPARATUS, AND SYSTEM FOR ESTIMATING A CORRECTED DIRECTIONAL ANGLE MEASURED BY A RADAR BY USING INPUT FROM A CAMERA
A method for estimating a corrected directional angle measured by a radar by using input from a camera comprises receiving radar detections of first objects in a scene and camera detections, which are simultaneous with the radar detections, of second objects in the scene. Each radar detection is indicative of a first directional angle and a distance of a respective first object in relation to the radar, and each camera detection is indicative of a direction of a respective second object in relation to the camera. Radar and camera detections which are detections of a same object in the scene are identified by comparing the received radar detections to the received camera detections, and a corrected first directional angle for the identified radar detection is estimated by using the direction of the identified camera detection and the known positions and orientations of the radar and the camera.
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
G01S 13/42 - Simultaneous measurement of distance and other coordinates
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
73.
RESOLVING A MULTIPATH AMBIGUITY IN A TDM-MIMO RADAR
A TDM MIMO FMCW radar comprises at least one row of physical receivers with a first spacing (dr) in a first direction, and further comprises a plurality of physical transmitters arranged with a second spacing (dt) in said first direction. To determine whether a peak in an angle spectrum corresponds to a direct reflection or a first-order multipath artefact, an inverse phase-shift vector corresponding to a phase ({circumflex over (ϕ)}1) of the peak is applied and a constant signal with the amplitude of the peak is subtracted. To the thus obtained intermediate signal (v), a further inverse phase-shift vector—now corresponding to an offset phase (Δ{circumflex over (ϕ)}) of the two leading peaks of the angle spectrum—after which a constant signal is subtracted. It is then detected whether the thus obtained test signal (w) has any non-noise content. If yes, the peak corresponds to a multipath artefact, and otherwise to a direct reflection.
A method for improving image quality of images captured by a camera system having an image sensor and implementing optical image stabilisation (OIS) comprises receiving image data representing an image captured by the camera system, receiving stabilisation position data from an OIS device in the camera system, the stabilisation position data indicating a position where an optical axis of the optical path intersects with the image sensor, and includes the step of applying a lens correction function to the received image data, wherein the application of the lens correction function is adjusted based on the received stabilisation position data, and outputting the corrected image data.
H04N 23/68 - Control of cameras or camera modules for stable pick-up of the scene, e.g. compensating for camera body vibrations
G03B 5/00 - Adjustment of optical system relative to image or object surface other than for focusing of general interest for cameras, projectors or printers
H04N 23/55 - Optical parts specially adapted for electronic image sensorsMounting thereof
H04N 23/67 - Focus control based on electronic image sensor signals
A method for object attribute classification in an image is provided, and includes obtaining a plurality of object proposals from an artificial neural network entity trained to localize and classify objects using a plurality of feature map layers associated with different spatial resolutions; identifying a main object proposal and one or more other proposals; ranking the feature map layers from a least significant to a most significant feature map layer, and determining an attribute class for a first attribute based on attribute class confidence scores of the main and other proposals, including taking the ranking of the feature map layers as well as object location overlaps into account for the determining. A corresponding device, computer program and computer program product are also provided.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/771 - Feature selection, e.g. selecting representative features from a multi-dimensional feature space
A method for determining similarity in appearance of objects in image frames of a video sequence, comprises: processing first raw image data applying first image processing settings; detecting a first object; extracting first feature vectors; processing second raw image data applying second image processing settings; detecting a second object; extracting second feature vectors; determining the similarity of the first and second objects by comparing the first feature vectors to the second feature vectors; if the first feature vectors and the second feature vectors differ by more than a first threshold, and the first image processing settings differ from the second image processing settings by more than a second threshold: re-processing the first raw image data corresponding applying the second image processing settings; extracting, from the first image area, updated first feature vectors; comparing the updated first feature vectors to the one or more second feature vectors.
Using a sequence of images depicting a traffic situation involving a plurality of moving vehicles recorded by a camera, a set of image event data are determined, where each image event data indicates a respective number of events related to the moving vehicles occurring during a respective imaging time interval. A set of incident event data are obtained from a database, where each incident event indicates a respective number of events occurring during a respective incident time interval, detected by a traffic event detector located at a known detector geographical position. It is determined, based on a matching procedure between the set of image event data and the set of incident event data, that the events associated with the set of image event data are the events associated with the set of incident event data, and thus the camera geographical position is associated with the detector geographical position.
A method of providing anonymized data for facilitating reidentification in a visual tracking system, the method comprising: detecting, in images obtained from a plurality of image sources, subareas which each contain a tracking target; computing, for each subarea, a feature vector which represents a visual appearance of the tracking target therein; for a first/second subgroup of the image sources, providing first/second reidentification data items by anonymizing each feature vector using a predefined one-way function modified by a first/second tracking-rights token, and disclosing the first/second reidentification data items annotated with locations of the respective subareas to a first/second tracking client, wherein the second tracking-rights token is distinct from the first tracking-rights token; and preventing access to the feature vectors.
Video tracks include positions and a classification relative to respective objects detected in a sequence of video image frames captured of a scene during a time period and the radar track includes positions and classification in relation to of an object detected in radar data captured for at least a part of the scene during the time period. An indication is obtained for each video track of the video tracks whether the radar track and the video track of the video tracks correspond based on a similarity according to a similarity measure. A second video track is ignored or deleted on condition that the radar track and a first video track correspond, the classifications associated with the radar track and the first video track correspond, the radar track and the second video track correspond, and the classifications associated with the radar track and the second video track do not correspond.
G01S 13/86 - Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
G01S 13/72 - Radar-tracking systemsAnalogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
G06V 20/40 - ScenesScene-specific elements in video content
A method estimates a radiance of an object and comprises: obtaining a thermal image of a scene comprising an apparent object region depicting the object; obtaining object data indicative of a location and an extension of an actual object region; determining a representative background radiance; obtaining a blur parameter indicative of a blur radius of a blur spot; determining a pixel value of a sample pixel of the apparent object region; determining for the sample pixel: an object radiance contribution factor based on a number of actual object pixels located within the blur range from the sample pixel, and a background radiance contribution factor based on a number of actual background pixels located within the blur range from the sample pixel; and estimating a diffraction-compensated radiance of the object based on the pixel value of the sample pixel, the representative background radiance, and the object and background radiance contribution factors.
A method for thermal image processing comprises obtaining a thermal image depicting a scene comprising objects; identifying a set of apparent object regions, each apparent object region includes a depiction of a respective object which is blurred due to diffraction, and each apparent object region being identified as a contiguous region of pixels having intensities differing from a representative background intensity by more than a threshold intensity, and exceeding a threshold size such that the apparent object region includes an actual object region and a blurred edge region and applying to each object region a contrast enhancement comprising: partitioning the blurred edge region into a background edge region and an object edge region intermediate the actual object region and the background edge region; and setting pixels of the object edge region to a representative object intensity determined from actual object pixels, and pixels of the background edge region.
Encoding and decoding of lidar data frames is presented, and in particular to entropy coding of lidar data wherein the context models used depend on the order in which the lidar sensor receives the lidar return signals. For example, an indication whether a lidar return signal with a particular index having a value i, 1≤i≤Y, corresponding to a sequential order based on a time of arrival of lidar return signals of the emitted ray, may be encoded as an entropy coded bit using a distinct context model for each possible value i of the index.
Encoding and decoding methods are employed for toggleable overlays in a video. An encoding method comprises receiving a plurality of image frames; determining one or more overlay image frames each comprising a plurality of toggleable overlays, each toggleable overlay being associated with an identifier. An image frame is associated with an overlay image frame of the one or more overlay image frames. Metadata is added to a header of the image frame, wherein the metadata comprises, for each toggleable overlay of the plurality of toggleable overlays: position data identifying a position of the toggleable overlay in the overlay image frame, size data identifying a size of the toggleable overlay in the overlay image frame, and identification data corresponding to the identifier of the toggleable overlay.
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/177 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
A computer-implemented method of tracking objects in a video sequence of a scene comprises determining a location of a sink in the scene where objects exit the scene and a location of a source where objects enter the scene; tracking a first object moving in the scene using a re-identification algorithm, wherein the first object is associated with a re-identification threshold of the re-identification algorithm; detecting that the first object has exited the scene at the sink; and responsive to detecting that the first object has exited the scene at the sink, adjusting the re-identification threshold associated with the first object such that a probability that the re-identification algorithm re-identifies a second object, entering the scene at the source after the first object has exited the scene at the sink, as the first object is reduced.
G06V 20/40 - ScenesScene-specific elements in video content
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
88.
METHOD AND SYSTEM FOR DIGITALLY SIGNING AUDIO AND VIDEO DATA
A method of digitally signing a video and audio sequence includes a first video digest generated by applying a digest algorithm to a first video portion of the video sequence, and a first audio digest is generated by applying a digest algorithm to a first audio portion of the audio sequence. A first video signature is generated by digitally signing the first video digest, and a first audio signature is generated by digitally signing the first audio digest. The first video and audio signatures are inserted in first target audio and video portions, respectively. A second video digest is generated from the first target video portion and first audio signature, and a second audio digest is generated from the first target audio portion and first video signature. Second video and audio signatures are generated by digitally signing the second video and audio digests, respectively.
A method and a video capturing system for controlling at least one infrared (IR) illumination source, illuminating an area captured by a camera, the method comprising modulating emission from the IR illumination source between first and second emission intensities at a first frequency during a first predetermined time period. Upon capturing images using an image sensor of the camera during the first predetermined time period, determining a radiation level indicator for each of a plurality of images captured by the image sensor. Further, evaluating a sequence of determined radiation level indicators to determine whether a frequency resulting from the modulated emission at the first frequency is detected in the sequence of radiation level indicators. If the frequency resulting from the modulated emission at the first frequency is determined as not detected in the sequence of radiation level indicators, the IR illumination source is stopped.
Controlling a camera includes determining a first camera setting for monitoring a scene under a first lighting condition, and a second camera setting for monitoring the scene under a second lighting condition. The method further includes obtaining a reference image that represents the scene under the second lighting condition, as captured in the first camera setting. While monitoring the scene in the first camera setting, detecting a change in the scene from the first to the second lighting condition. The detecting the change includes performing a comparison between first image feature data derived from a first image of the scene captured with the camera set to the first camera setting, after the change from the first to the second lighting condition, and reference image feature data derived from the reference image, to determine that the first image feature data matches the reference image feature data.
There is provided a method and an encoder for inter-encoding an image frame in a sequence of image frames. The method comprises obtaining a compression level for each pixel block of the image frame, inter-encoding the image frame in a first encoding pass using the obtained compression level and identifying pixel blocks in the image frame that were intra-coded in the first encoding pass and for which the obtained compression level exceeds a compression level threshold. The method further comprises lowering the compression level for the identified pixel blocks, and inter-encoding the image frame in a second encoding pass using the lowered compression level for the identified pixel blocks and the obtained compression level for each remaining pixel block.
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/167 - Position within a video image, e.g. region of interest [ROI]
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
A method of stitching image data from one or more image sensors arranged to acquire image data depicting at least partly overlapping views of a scene comprises obtaining sets of image data representing a blending region; dividing each set of image data into portions of different interest levels; for each portion of image data, determining one or more image frequency bands based on the interest level of the portion, and obtaining image data of the determined one or more image frequency bands from the portion of image data; and blending the first set of image data and the second set of image data by multi-band blending, wherein only the obtained image data of the determined one or more image frequency bands are blended for each portion of image data.
G06T 7/90 - Determination of colour characteristics
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
A method of encoding images in a video, comprises: acquiring an original image from an image sensor of a video camera; encoding the original image using a generative image model, thereby obtaining a first encoded image; decoding the image to obtain a first decoded image; identify a region of interest (ROI) of the original image; for each ROI: perform an encoding quality check by comparing several reference points in the ROI of the original image against corresponding reference points in the ROI of the first decoded image, thereby obtaining a difference; if the difference is greater than a threshold, encoding the ROI using a non-generative image model, thereby obtaining a non-generative encoded image area; providing final encoded image data comprising a) the non-generative encoded image areas for the ROI having a difference greater than the threshold and b) the first encoded image for a remaining part of the original image.
H04N 19/154 - Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
H04N 19/119 - Adaptive subdivision aspects e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/17 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
H04N 19/186 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
H04N 19/20 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video object coding
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
96.
VIDEO STREAM ENCODING BOTH OVERVIEW AND REGION-OF-INTEREST(S) OF A SCENE
A method for encoding a video stream includes obtaining images of a scene captured by a camera at a first resolution; identifying regions of interest (ROIs) in an image; adding, as part of an encoded video stream, a first video frame encoding at least part of the image at a second resolution lower than the first resolution; adding a second video frame marked as a no-display frame, and being an inter-frame referencing the first video frame with motion vectors for upscaling of the ROIs; adding a third video frame encoding the ROIs at a third resolution higher than the second resolution, and being an inter-frame referencing the second video frame.
H04N 19/33 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability in the spatial domain
H04N 19/105 - Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
H04N 19/167 - Position within a video image, e.g. region of interest [ROI]
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
An image processing device generates an output image with masking of objects of classes. An input image is downscaled to an object detection image having a lower resolution than the input image and lower than the output image. The object detection image is input to an object detection module and confidence scores for pixel areas are received from the object detection module. Each confidence score indicates a probability that the pixel area relates to an object to be masked. Based on the input image, an intermediate image is generated having a higher resolution than the object detection image resolution and an adaptive masking threshold is set such that the greater the ratio between the output image resolution and the object detection image resolution, the lower the masking threshold. The output image is then generated by masking pixel areas of the intermediate image.
G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A method for processing of data by applying a first algorithm in a system comprising a server and a client device comprises at the client device: continuously receiving data; dividing the received data into M>1 subsets of data, each subset of data comprising, or being derived from, sensor data captured by a sensor during a capture time interval at a plurality of points in time; determining for each subset whether the processing of the subset should be performed by the client device or by the server, and upon determining that the processing should be performed by the client device, processing the subset into processed data by applying the first algorithm to the subset, and transmitting the processed data to the server, and upon determining that processing should be performed by the server, transmitting the subset of data to the server.
This disclosure relates to methods systems and non-transitory computer-readable storage mediums for distributing load in a multi-chip image processing unit for processing image data into processed image data. An example method comprises receiving first image data, analysing the first image data using a first algorithm, the first algorithm performing a set number of operations for a given size of image data input to the first algorithm, and outputs at least one characteristic of the first image data; using the at least one characteristic to estimate use of memory bandwidth in the first and second chip when processing the first image data into processed image data; and distributing processing of the first image data between the first and the second chip such that the estimated use of memory bandwidth is distributed evenly.
H04N 19/127 - Prioritisation of hardware or computational resources
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
H04N 23/80 - Camera processing pipelinesComponents thereof
