Template points associated with a template are obtained. An image of the template is captured using a camera. The template points of the template are mapped to image points in the image of the template to determine a 2-dimensional transformation matrix (H2D). Pose transform is obtained based on an initial projection matrix resulting from a prior calibration of the camera and the 2-dimensional transformation matrix (H2D), the pose transform having an incomplete rotation matrix. The pose transform is normalized to obtain an updated pose transform having a complete rotation matrix. An affine transform is applied using the complete rotation matrix and a translation vector of the normalized pose transform to the initial projection matrix to obtain an updated projection matrix.
Embodiments are disclosed for determining a ball impact location on a surface of a striking object. In some embodiments, a method comprises: capturing a series of images including at least three images of a striking object prior to impact with a stationary ball; determining, based on center coordinates of surfaces of the striking object captured in the at least three images, a circumference of a circle; shifting the ball along the circumference of the circle to a virtual position such that the ball at the virtual position is in contact with a first surface of the striking object; determining a first impact location on the first surface of the striking object in contact with the shifted ball at the virtual position.
An image of an object is received, the image being captured by a camera. Two-dimensional (2D) image points on perimeters of the object in the image are determined. Using a rotation component of a homography matrix, the 2D image points are converted into corresponding three-dimensional (3D) points on a 3D conic section that passes through a center of the camera and the perimeters of the object. The 3D points on the 3D conic section are normalized. A principal direction to a center of the object is determined, based on the normalized 3D points on the 3D conic section. 2D object center is determined based on the principal direction and the rotation component of the homography matrix.
A spin-estimation system may include an image-capturing sensor positioned and configured to capture images of an object within a field of view of the image-capturing sensor. The spin-estimation system may be configured to perform one or more operations to analyze spin properties of the object. The operations may include setting an image capture framerate that corresponds to a minimum spin motion of the object, printing an orientation marker on an outer surface of the object, and capturing, by the image-capturing sensor at the set image capture framerate, images of the object after starting motion of the object. The operations may include isolating the object in each image to generate isolated object images. The operations may include generating an object marker segmentation map based on the isolated object images. A spin rate and a spin axis may be estimated based on the object marker segmentation map using deep learning approaches.
The present disclosure relates to an object detection using deep learning. The method comprises extracting with a machine learning model, a first region from an image and pooling with the machine learning model, the first region to a second region that is smaller than the first region. The method further comprises predicting with the machine learning model, a geometric center and radius of a blob of pixels in the second region and a confidence score associated with the predicting; and classifying, with the machine learning model, the blob of pixels as a ball based on the confidence score.
G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p. ex. en projetant les caractéristiques sur des hyperplans
Embodiments are disclosed for object detection using deep learning. In some embodiments, a method comprises: extracting, with a machine learning model, a first region from an image; pooling, with the machine learning model, the first region to a second region that is smaller than the first region; predicting, with the machine learning model, a geometric center and radius of a blob of pixels in the second region and a confidence score associated with the predicting; and classifying, with the machine learning model, the blob of pixels as a ball based on the confidence score.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
7.
Camera and radar fusion for determining position of projectile in motion
Disclosed are embodiments for determining the position and speed of a projectile in three-dimensional world coordinates using a single camera and radar. In some embodiments, a method comprises: determining a zero radial speed time of the projectile based on radar measurements of the projectile; determining a radial speed of the projectile at a particular time based on the radar measurements; determining a radial speed slope at the particular time based on the radial speed measurements; determining a two-dimensional image point of the projectile at the particular time; determining a depth scale coefficient at the particular time based on the zero radial speed time, the radial speed, the radial speed slope, the image point, and a homographic transformation; and determining a position of the projectile in space at the particular time based on a three-dimensional position of the camera, the depth scale coefficient, the image point and the homographic transformation.
Pixel intensity differences between pixel values of a region in a first image frame and the region in a second image frame are determined, where the region in the first image frame includes an image of a spherical object, with no overlapping spherical object in the second image frame. Based on the pixel intensity differences, the region of the first image frame is thresholded into foreground pixels and background pixels until the background pixels make up more than a predefined percentage of the region of the first image frame. From the background pixels, a subset of the background pixels located along a plurality of paths that radially extend outward from a center of the region in different angular degrees is selected. The subset of the background pixels is filtered by applying a filter, where the filtered subset is detected as edge pixels.
G06T 7/136 - DécoupageDétection de bords impliquant un seuillage
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
In a first step, a time series of images are resized to a smaller dimension, and the smaller images are fed into a first classifier that is trained to classify as a ball any objects in the smaller images that resemble a ball. In a second step, the smaller images are mapped back to the series of images, and regions in the series of images that contain the mapped ball are cropped from the series of images. The mapped ball is shifted based on a velocity of the mapped ball in the cropped regions, and the second classifier regresses center coordinates and a radius of the shifted ball, classifies whether the shifted ball is the ball based on a confidence score, and updates the shifted ball in the cropped regions based on the regressed center coordinates and radius.
G06T 3/4046 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement utilisant des réseaux neuronaux
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p. ex. en projetant les caractéristiques sur des hyperplans
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
In a first step, a time series of images are resized to a smaller dimension, and the smaller images are fed into a first classifier that is trained to classify as a ball any objects in the smaller images that resemble a ball. In a second step, the smaller images are mapped back to the series of images, and regions in the series of images that contain the mapped ball are cropped from the series of images. The mapped ball is shifted based on a velocity of the mapped ball in the cropped regions, and the second classifier regresses center coordinates and a radius of the shifted ball, classifies whether the shifted ball is the ball based on a confidence score, and updates the shifted ball in the cropped regions based on the regressed center coordinates and radius.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Embodiments are disclosed for determining a spin rate and axis of a ball using deep learning. In some embodiments, a method comprises: training a deep learning network on training images of spinning balls, each spinning ball having at least one feature point in a time series that forms a two-dimensional (2D) ellipse image in a 2D plane; capturing, with at least one camera, a series of images of a ball; predicting, with the trained deep learning network, spin measurements associated with the ball based on the series of images; determining a spin rate of the ball based on the spin measurements; determining coefficients of a 2D ellipse model based on the spin measurements and the spin rate; and determining, with the at least one processor, a spin axis of the ball in 3D space based on the 2D ellipse model and the spin rate.
Embodiments are disclosed for a range-gated imager. In some embodiments, a method comprises: transmitting, with a single-tone continuous wave (STCW) radar, a signal; receiving, with the STCW radar, a return signal from a projectile impinged by the radar signal; counting, with a measuring apparatus, a specified number of periods of non-ambiguity range based on the return signal, performing a flashing operation; and gating or triggering, by the measuring apparatus, an imager to capture an image of the projectile in response to the count reaching the specified number of periods.
Embodiments are disclosed for a range-gated imager. In some embodiments, a method comprises: transmitting, with a single-tone continuous wave (STCW) radar, a signal; receiving, with the STCW radar, a return signal from a projectile impinged by the radar signal; counting, with a measuring apparatus, a specified number of periods of non-ambiguity range based on the return signal, performing a flashing operation; and gating or triggering, by the measuring apparatus, an imager to capture an image of the projectile in response to the count reaching the specified number of periods.
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
Embodiments are disclosed for a range-gated imager. In some embodiments, a method comprises transmitting, with a multi-tone continuous wave (MTCW) radar, a radar signal comprising a first tone and a second tone, where the first tone and the second tone are separated by a frequency gap; receiving, with the MTCW radar, a return signal from a projectile impinged by the radar signal; detecting, with a measuring apparatus, a zero crossing of a phase difference between the first and second tones; and responsive to detecting the zero crossing, gating or triggering, by the measuring apparatus, an imager to capture an image of the projectile.
G01S 13/36 - Systèmes pour mesurer la distance uniquement utilisant la transmission d'ondes continues, soit modulées en amplitude, en fréquence ou en phase, soit non modulées avec comparaison en phase du signal reçu avec le signal transmis au même moment
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
15.
DEEP LEARNING METHOD OF DETERMINING GOLF SWING AND GOLF BALL PARAMETERS FROM RADAR SIGNAL AND IMAGE DATA
A launch-monitoring system that models a portion of a golf club, golf swing, and golf ball may include a camera and a radar positioned orthogonally to a swing direction of the golf club. A series of images of the golf ball are collected during and after the golf club contacts the golf ball by the camera. The golf swing is captured by the radar. The images are converted into parameterized motion representations, and the radar signal is converted into time-frequency images, which are sent to a convolutional neural network. The convolutional neural network outputs golf club parameters, golf swing parameters, and golf ball parameters, which generate a visual model of the golf club, golf swing, and golf ball in a virtual space. The parameterized motion representations of the golf ball and the time frequency images of the golf swing are not correlated and operate independently from each other.
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G01S 13/88 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
A method for calibrating a camera without the decomposition of camera parameters into extrinsic and intrinsic components is provided. Further, there is provided a method for tracking an object in motion comprising capturing one or more image frames of an object in motion, using one or more calibrated cameras that have been calibrated according to a calibration method that generates and uses a respective transformation matrix for mapping three-dimensional (3D) real world model features to corresponding two-dimensional (2D) image features. The tracking method further comprises determining, using a hardware processor, motion characteristics of the object in motion based on the captured one or more image frames from each one or more calibrated cameras, the determining of the motion characteristics based on implicit intrinsic camera parameters and implicit extrinsic camera parameters of the respective transformation matrix from each respective one or more calibrated cameras.
An image taken of an object in motion is received. At least three markers on the object in motion in the image are detected. Image points of the three markers are determined. Based on the image points of the three markers, depths of the object in motion at the image points are determined, where the depths are determined relative to an image plane of the image. Using the image points and the depths, three-dimensional (3D) world points representing a position of the object in motion in a 3D real world coordinate are determined.
Disclosed are embodiments for determining the position and speed of a projectile in 3D world coordinates using a calibrated camera and radar. In some embodiments, a method comprises: capturing a sequential set of images of a projectile in motion; determining a radial velocity of the projectile; determining a set of two-dimensional (2D) image points representing respective locations of the projectile in the sequential set of images; determining respective depths of the 2D image points in their respective 2D image planes based on the 2D image points, time intervals between the captured 2D images, the radial velocity and a rotational part of a homographic transformation, the homographic transformation configured to project three-dimensional (3D) world coordinates to the 2D image planes; and determining a set of 3D world points of the projectile in the 3D world coordinates based on the 2D image points, the respective depths thereof and the homographic projection.
A method for calibrating a camera without the decomposition of camera parameters into extrinsic and intrinsic components is provided. Further, there is provided a method for tracking an object in motion comprising capturing one or more image frames of an object in motion, using one or more calibrated cameras that have been calibrated according to a calibration method that generates and uses a respective transformation matrix for mapping three-dimensional (3D) real world model features to corresponding two-dimensional (2D) image features. The tracking method further comprises determining, using a hardware processor, motion characteristics of the object in motion based on the captured one or more image frames from each one or more calibrated cameras, the determining of the motion characteristics based on implicit intrinsic camera parameters and implicit extrinsic camera parameters of the respective transformation matrix from each respective one or more calibrated cameras.
Embodiments are disclosed for determining a three-dimensional (3D) trajectory, spin rate and spin axis of a basketball in flight. In some embodiments, a method comprises: capturing, using a first camera, a first set of images of a basketball in motion after the basketball is released by a player; capturing, using a second camera, a second set of images of the basketball when it contacts a rim of a basketball hoop; measuring, using a radar, radar data associated with the basketball; and generating, using the first and second sets of images, an observed three-dimensional trajectory of the basketball, based on two-dimensional position data determined from the first and second sets of images, intrinsic parameters of the first and second cameras, extrinsic parameters of the first and second cameras and the radar data.
Automatic club recognition during a golf swing is provided. At least one processor receives a series of images of a golf club captured during a golf swing. At least one processor detects from the series of images one or more sticker labels placed on the golf club. At least one processor classifies a golf club type of the golf club based on recognizing a marker, e.g., a unique or particular marker, coded on the one or more sticker labels, the marker for representing a specific type of golf club.
A63B 60/42 - Dispositifs de mesure, de vérification, de correction ou de personnalisation des caractéristiques inhérentes aux clubs de golf, battes, raquettes ou analogues, p. ex. mesure du couple de torsion maximale que le manche d'une batte peut supporter
A calibration mark apparatus includes a support having at least one wall with at least one calibration pattern on the least one wall and at least one attachment portion extending from the least one wall. The attachment portion may be a flap extending from at least one wall for attaching the support to a surface or a net. The support may be a three-dimensional (3D) structure. The calibration mark apparatus may have walls that are foldable along a common edge into a single plane. The calibration mark apparatus may have a base connecting the walls including raised tab portions arranged to fit into slots in the flap. The flap may include openings for attaching multiple supports together by a rope that is arranged to feed through the openings. The calibration mark apparatus may include a bracket to mount the support to a surface or a net by clips.
G01B 21/04 - Dispositions pour la mesure ou leurs détails, où la technique de mesure n'est pas couverte par les autres groupes de la présente sous-classe, est non spécifiée ou est non significative pour mesurer la longueur, la largeur ou l'épaisseur en mesurant les coordonnées de points
A calibration mark apparatus includes a support having at least one wall with at least one calibration pattern on the least one wall and at least one attachment portion extending from the least one wall. The attachment portion may be a flap extending from at least one wall for attaching the support to a surface or a net. The support may be a three-dimensional (3D) structure. The calibration mark apparatus may have walls that are foldable along a common edge into a single plane. The calibration mark apparatus may have a base connecting the walls including raised tab portions arranged to fit into slots in the flap. The flap may include openings for attaching multiple supports together by a rope that is arranged to feed through the openings. The calibration mark apparatus may include a bracket to mount the support to a surface or a net by clips.
Embodiments are disclosed for determining the spin rate and the spin axis of a ball in flight. In some embodiments, a method comprises: capturing, with an image sensor, a time series of images of a ball in flight and corresponding image capture times; inputting, with at least one processor, the image frames and the image capture times into a machine learning model; and predicting, with the at least one processor, a spin axis and a spin rate of the ball based on the machine learning model.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
26.
DETERMINATION OF SPIN RATE AND SPIN AXIS OF A BALL IN FLIGHT
Embodiments are disclosed for determining the spin rate and the spin axis of a ball in flight. In some embodiments, a method comprises: capturing, with an image sensor, a time series of images of a ball in flight and corresponding image capture times; inputting, with at least one processor, the image frames and the image capture times into a machine learning model; and predicting, with the at least one processor, a spin axis and a spin rate of the ball based on the machine learning model.
A method may include collecting first and second image data of an object motion, the first and second image data respectively including first and second frames captured by a first and a second camera. The method may include identifying an object included in the first and second frames and modeling two-dimensional pose estimations of the object for each identified frame. The two-dimensional pose estimations may indicate coordinate positions of the object features that contribute to the object motion. The method may include generating a first and a second three-dimensional joint heatmap corresponding to the object identified in the first and second frames based on features indicated in the two-dimensional pose estimations. The method may include determining a time delay between the first and second cameras based on the three-dimensional joint heatmaps and generating a motion journal that summarizes the motion associated with the object based on the time delay.
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06V 40/10 - Corps d’êtres humains ou d’animaux, p. ex. occupants de véhicules automobiles ou piétonsParties du corps, p. ex. mains
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
Produits et services
Sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters working with doppler radar to track golf ball flight and transmit results through wireless radio and wireless networking technology to a downloadable software application, all sold as a unit for measuring, providing data visualization and reporting on golf ball and club performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, instructors, coaches, manufacturers and fitters of clubs and balls, all for the purpose of use in golf practice, golf instruction, golf entertainment, golf course simulation and golf virtual play and competitions, golf club and golf ball fitting, golf club design, and for use in sound and video recording and playback Transmission of sound, video and information from high speed digital photographic equipment, all featuring live or recorded materials in the field of sports performance
A spin-estimation system may include an image-capturing sensor positioned and configured to capture images of an object within a field of view of the image-capturing sensor. The spin-estimation system may be configured to perform one or more operations to analyze spin properties of the object. The operations may include setting an image capture framerate that corresponds to a minimum spin motion of the object, printing an orientation marker on an outer surface of the object, and capturing, by the image-capturing sensor at the set image capture framerate, images of the object after starting motion of the object. The operations may include isolating the object in each image to generate isolated object images. The operations may include generating an object marker segmentation map based on the isolated object images. A spin rate and a spin axis may be estimated based on the object marker segmentation map using deep learning approaches.
A system and method for determining a position an object moving along a trajectory crosses a target plane, by obtaining a plurality of images of an object moving along a trajectory in an environment, the plurality of images capturing the object at different positions along the trajectory, obtaining a plurality of images of an object moving along a trajectory in an environment, the plurality of images capturing the object at different positions along the trajectory, the plurality of images being obtained from two or more cameras positioned at different positions in the environment, each of the two or more cameras having a field of view (FOV) that includes the trajectory of the moving object and a target plane, the target plane being a plane that the moving object crosses during movement along the trajectory, each of the two or more cameras obtaining the images at a different timing.
Embodiments are disclosed for a range-gated imager. In some embodiments, a method comprises transmitting, with a multi-tone continuous wave (MTCW) radar, a radar signal comprising a first tone and a second tone, where the first tone and the second tone are separated by a frequency gap; receiving, with the MTCW radar, a return signal from a projectile impinged by the radar signal; detecting, with a measuring apparatus, a zero crossing of a phase difference between the first and second tones; and responsive to detecting the zero crossing, gating or triggering, by the measuring apparatus, an imager to capture an image of the projectile.
A method for calibrating a camera without the decomposition of camera parameters into extrinsic and intrinsic components is provided. Further, there is provided a method for tracking an object in motion comprising capturing one or more image frames of an object in motion, using one or more calibrated cameras that have been calibrated according to a calibration method that generates and uses a respective transformation matrix for mapping three-dimensional (3D) real world model features to corresponding two-dimensional (2D) image features. The tracking method further comprises determining, using a hardware processor, motion characteristics of the object in motion based on the captured one or more image frames from each one or more calibrated cameras, the determining of the motion characteristics based on implicit intrinsic camera parameters and implicit extrinsic camera parameters of the respective transformation matrix from each respective one or more calibrated cameras.
An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.
A method includes receiving motion data of a user in an environment with respect to a plurality of instances of a first action by the user, determining a kinematic movement based on receiving the motion data, analyzing the kinematic movement using a neural network, obtaining a plurality of outcome types with respect to the first action of the user, correlating the kinematic movement with the at least one indication of the outcome type with respect to the first action, classifying an outcome of the first action as at least one of the plurality of outcome types, determining which of the kinematic movements of the user result in the at least one of the plurality of outcome types, and providing instructions to the user to alter the determined kinematic movements of the user that result in the at least one of the plurality of outcome types.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, and high speed digital photographic equipment in the nature of digital cameras, digital photo image converters, connecting devices for photographic equipment in the nature of cable connectors, and recorded computer software, all sold as a unit for measuring pitching performance when a softball or baseball is pitched, softball and baseball ball and bat performance when a ball is struck, which then provides softball and baseball ball flight and bat motion data to baseball and softball players, softball and baseball ball flight data and motion data to pitchers, instructors, manufacturers and fitters of balls and bats, for the purpose of use in softball and baseball practice and virtual play, softball and baseball instruction and softball and baseball bat fitting and bat design
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, and high speed digital photographic equipment in the nature of digital cameras, digital photo image converters, connecting devices for photographic equipment in the nature of cable connectors, and recorded computer software, all sold as a unit for measuring pitching performance when a softball or baseball is pitched, softball and baseball ball and bat performance when a ball is struck, which then provides softball and baseball ball flight and bat motion data to baseball and softball players, softball and baseball ball flight data and motion data to pitchers, instructors, manufacturers and fitters of balls and bats, for the purpose of use in softball and baseball practice and virtual play, softball and baseball instruction and softball and baseball bat fitting and bat design
A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.
A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.
A launch-monitoring system that models a portion of a golf club, golf swing, and golf ball may include a camera and a radar positioned orthogonally to a swing direction of the golf club. A series of images of the golf ball are collected during and after the golf club contacts the golf ball by the camera. The golf swing is captured by the radar. The images are converted into parameterized motion representations, and the radar signal is converted into time-frequency images, which are sent to a convolutional neural network. The convolutional neural network outputs golf club parameters, golf swing parameters, and golf ball parameters, which generate a visual model of the golf club, golf swing, and golf ball in a virtual space. The parameterized motion representations of the golf ball and the time frequency images of the golf swing are not correlated and operate independently from each other.
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G01S 13/88 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques
A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.
A63B 71/06 - Dispositifs indicateurs ou de marque pour jeux ou joueurs
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 11/20 - Traçage à partir d'éléments de base, p. ex. de lignes ou de cercles
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/00 - ScènesÉléments spécifiques à la scène
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters, connecting devices for photographic equipment in the nature of cable connectors, and recorded computer software, all sold as a unit for measuring, providing data visualization and reporting on golf ball and club performance when a ball is struck, on pitching performance when a softball or baseball is pitched, or on softball and baseball ball and bat performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, instructors, coaches, manufacturers and fitters of clubs and balls, softball and baseball ball flight and bat motion data to baseball and softball players, instructors, coaches, manufacturers and fitters of bats, or softball and baseball ball flight data and motion data to pitchers, instructors, coaches, manufacturers and fitters of balls, all for the purpose of use in golf practice, golf instruction, golf entertainment, golf course simulation and golf virtual play and competitions, golf club and golf ball fitting, golf club design, softball and baseball practice, instruction, entertainment, play simulation, virtual play and competitions, and softball and baseball ball and bat fitting and design; Sound and video recording and playback machines; Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters working with doppler radar to track golf ball flight and transmit results through wireless radio and wireless networking technology to a downloadable software application, all sold as a unit for measuring, providing data visualization and reporting on golf ball and club performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, instructors, coaches, manufacturers and fitters of clubs and balls, all for the purpose of use in golf practice, golf instruction, golf entertainment, golf course simulation and golf virtual play and competitions, golf club and golf ball fitting, golf club design, and for use in sound and video recording and playback; Downloadable application software for storing, editing and sharing videos among users; downloadable application software for assigning, sharing, managing and tracking tasks assigned among users; downloadable application software for storing, sharing, tracking and managing schedules among users; downloadable application software for storing, maintaining, tracking and comparison of data among users; downloadable software application for mobile phones and tablets for providing a database of sports performance measurement data in the field of golf, for tracking, managing and sharing sports performance measurement data in the field of golf between application users, and for golf course simulation and for golf course virtual play; Portable sports performance measurement equipment, namely, downloadable mobile phone application software, USB cables, portable stand for mobile phones, carrying case and user manuals, all sold as a unit, for video recording and storage, video playback, tracking gps location information, measuring, analyzing and storing golf ball and club performance information, namely, carry distance, ball speed and direction, club speed and direction, smash factor, ball angle, club angle and ball location Software as a service (SAAS) featuring software for measuring, evaluating, tracking and improving sports performance skills; Providing temporary use of non-downloadable software for storing, editing and sharing videos among users; providing temporary use of non-downloadable software for assigning, sharing, managing and tracking tasks assigned among users; providing temporary use of non-downloadable software for storing, sharing, tracking and managing schedules among users; providing temporary use of non-downloadable software for storing, maintaining, tracking and comparison of data among users
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
Produits et services
Portable sports performance measurement equipment, namely,
computer hardware, high speed digital photographic equipment
being digital cameras, and digital photo image converters
working with doppler radar to track golf ball flight and
transmit results through Bluetooth and WIFI to a software
application, all sold as a unit for measuring, providing
data visualization and reporting on golf ball and club
performance when a ball is struck, and then provides golf
ball flight data and club motion data to golfers,
instructors, coaches, manufacturers and fitters of clubs and
balls, all for the purpose of use in golf practice, golf
instruction, golf entertainment, golf course simulation and
golf virtual play and competitions, golf club and golf ball
fitting, golf club design, includes sound and video
recording and playback. Transmission of sound, video and information from high speed
digital photographic equipment, all featuring live or
recorded materials in the field of sports performance.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters working with doppler radar to track golf ball flight and transmit results through wireless radio and wireless networking technology to a downloadable software application, all sold as a unit for measuring, providing data visualization and reporting on golf ball and club performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, instructors, coaches, manufacturers and fitters of clubs and balls, all for the purpose of use in golf practice, golf instruction, golf entertainment, golf course simulation and golf virtual play and competitions, golf club and golf ball fitting, golf club design, and for use in sound and video recording and playback
46.
DEEP LEARNING METHOD OF DETERMINING GOLF CLUB PARAMETERS FROM BOTH RADAR SIGNAL AND IMAGE DATA
An example method of modeling a portion of a golf club and a golf swing includes scanning the golf club to obtain scanning information, training a convolutional neural network using the scanning information, using at least one camera to obtain a series of images, converting the series of images into parameterized motion representations, using at least one radar to obtain a radar signal, converting the radar signal into time-frequency images, inputting the parameterized motion representations and the time-frequency images into the convolutional neural network, receiving golf club parameters and golf swing parameters as an output of the convolutional neural network, and generating a visual model of the golf club and the golf swing in a virtual space using the golf club parameters and the golf swing parameters.
A63B 69/36 - Appareils d'entraînement ou appareils destinés à des sports particuliers pour le golf
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
A63B 71/06 - Dispositifs indicateurs ou de marque pour jeux ou joueurs
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
47.
DEEP LEARNING METHOD OF DETERMINING GOLF CLUB PARAMETERS FROM BOTH RADAR SIGNAL AND IMAGE DATA
An example method of modeling a portion of a golf club and a golf swing includes scanning the golf club to obtain scanning information, training a convolutional neural network using the scanning information, using at least one camera to obtain a series of images, converting the series of images into parameterized motion representations, using at least one radar to obtain a radar signal, converting the radar signal into time-frequency images, inputting the parameterized motion representations and the time-frequency images into the convolutional neural network, receiving golf club parameters and golf swing parameters as an output of the convolutional neural network, and generating a visual model of the golf club and the golf swing in a virtual space using the golf club parameters and the golf swing parameters.
An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.
G01P 3/48 - Dispositifs caractérisés par l'utilisation de moyens électriques ou magnétiques pour mesurer la vitesse angulaire en mesurant la fréquence du courant ou de la tension engendrés
A63B 69/36 - Appareils d'entraînement ou appareils destinés à des sports particuliers pour le golf
49.
Intelligent analysis and automatic grouping of activity sensors
A method may include obtaining sensor data from one or more activity sensors, each of the activity sensors being coupled to a respective area of a sports user. The method may include obtaining image data of the sports user and each of the activity sensors coupled to the sports user. The method may include identifying, by a machine learning module and based on the sensor data and the image data, a respective muscle associated with each respective area to which the activity sensors are coupled. The method may include identifying movement of the sports user based on the sensor data from the activity sensors and the identified body part. The method may include analyzing the identified movement of the sports user including evaluating a body posture of the sports user, identifying one or more movement patterns of the sports user, and/or performing an injury assessment for the sports user.
A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.
An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.
A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.
A method may include obtaining sensor data from one or more activity sensors, each of the activity sensors being coupled to a respective area of a sports user. The method may include obtaining image data of the sports user and each of the activity sensors coupled to the sports user. The method may include identifying, by a machine learning module and based on the sensor data and the image data, a respective muscle associated with each respective area to which the activity sensors are coupled. The method may include identifying movement of the sports user based on the sensor data from the activity sensors and the identified body part. The method may include analyzing the identified movement of the sports user including evaluating a body posture of the sports user, identifying one or more movement patterns of the sports user, and/or performing an injury assessment for the sports user.
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters working with doppler radar to track golf ball flight and transmit results through wireless radio and wireless networking technology to a downloadable software application, all sold as a unit for measuring, providing data visualization and reporting on golf ball and club performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, instructors, coaches, manufacturers and fitters of clubs and balls, all for the purpose of use in golf practice, golf instruction, golf entertainment, golf course simulation and golf virtual play and competitions, golf club and golf ball fitting, golf club design, and for use in sound and video recording and playback Transmission of sound, video and information from high speed digital photographic equipment, all featuring live or recorded materials in the field of sports performance
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely,
computer hardware, high speed digital photographic
equipment, namely, digital cameras, digital photo image
converters, and connecting devices for photographic
equipment in the nature of cable connectors and computer
software, all sold as a unit for measuring golf ball and
club performance when a ball is struck, pitching performance
when a softball or baseball is pitched, softball and
baseball ball and bat performance when a ball is struck,
which then provides golf ball flight data and club motion
data to golfers, softball and baseball ball flight and bat
motion data to baseball and softball players, softball and
baseball ball flight data and motion data to pitchers,
instructors, manufacturers and fitters of clubs, balls and
bats, for the purpose of use in golf practice and virtual
play, softball and baseball practice and virtual play, golf,
softball and baseball instruction, golf club fitting, golf
club design, and softball and baseball bat fitting and bat
design.
56.
Measurement and reconstruction of the golf launching scene in 3D
A method, including scanning a golf club to obtain scanning information; inputting the scanning information into a processing system; using at least one camera positioned behind and in-line to a golf swing direction and at least one lighting unit to obtain a series of images of a golf club during the golf swing; converting the series of images into parameterized motion representations; using at least one radar to obtain a radar signal; inputting the parameterized motion representations and the radar signal into the processing system; receiving golf club parameters and golf swing parameters as an output of the processing system; and generating a visual model of the golf club and the golf swing in a virtual space using the golf club parameters and the golf swing parameters.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters, connecting devices for photographic equipment in the nature of cable connectors, and recorded computer software, all sold as a unit for measuring golf ball and club performance when a ball is struck, pitching performance when a softball or baseball is pitched, or softball and baseball ball and bat performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, softball and baseball ball flight and bat motion data to baseball and softball payers, or softball and baseball ball flight data and motion data to pitchers, instructors, manufacturers and fitters of clubs, balls and bats, all for the purpose of use in golf practice and virtual play, softball and baseball practice and virtual play, golf, softball and baseball instruction, golf club fitting, golf club design, and softball and baseball bat fitting and bat design
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment being digital cameras, and digital photo image converters, connecting devices for photographic equipment in the nature of cable connectors, and recorded computer software, all sold as a unit for measuring golf ball and club performance when a ball is struck, pitching performance when a softball or baseball is pitched, or softball and baseball ball and bat performance when a ball is struck, and then provides golf ball flight data and club motion data to golfers, softball and baseball ball flight and bat motion data to baseball and softball payers, or softball and baseball ball flight data and motion data to pitchers, instructors, manufacturers and fitters of clubs, balls and bats, all for the purpose of use in golf practice and virtual play, softball and baseball practice and virtual play, golf, softball and baseball instruction, golf club fitting, golf club design, and softball and baseball bat fitting and bat design
A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
A63B 71/06 - Dispositifs indicateurs ou de marque pour jeux ou joueurs
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/00 - ScènesÉléments spécifiques à la scène
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06T 11/20 - Traçage à partir d'éléments de base, p. ex. de lignes ou de cercles
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
Systems and methods are disclosed to measure a PPG signal. In some embodiments, a method may include capturing a plurality of frames of a subject; tracking the position of a region of interest of the subject in each of the plurality of frames; creating a first time series signal, a second time series signal, and third time series signal corresponding with respective color channels of the plurality of frames; normalizing the first, second, and third time series signals, combining the normalized first time series signal, the normalized first time series signal, and the normalized first time series signal into a combined signal; creating a spectral signal from the combined signal; and extracting the PPG signal from the spectral signal.
A61B 5/024 - Mesure du pouls ou des pulsations cardiaques
A61B 5/00 - Mesure servant à établir un diagnostic Identification des individus
A61B 5/103 - Dispositifs de mesure pour le contrôle de la forme, du dessin, de la dimension ou du mouvement du corps ou de parties de celui-ci, à des fins de diagnostic
Operations of the present disclosure may include receiving a group of images taken by a camera over time in an environment. The operations may also include identifying a first position of an object in a target region of the environment in a first image of the group of images and identifying a second position of the object in a second image of the group of images. Additionally, the operations may include determining an estimated trajectory of the object based on the first position of the object and the second position of the object. The operations may further include, based on the estimated trajectory, estimating a ground position in the environment associated with a starting point of the estimated trajectory of the object. Additionally, the operations may include providing the ground position associated with the starting point of the estimated trajectory of the object for display in a graphical user interface.
A method including detecting an object within a field of view of a radar using a radar signal; tracking movement of the object through the field of view of the radar; triggering a camera to capture a plurality of images of the object based on the movement of the object; detecting the object in the plurality of images; combining data of the radar signal with data of the camera to estimate a position of the object; identifying a radar signal track generated by the motion of the object based on the combined data; and estimating a trajectory of the object based on identifying the radar signal track.
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
H04N 23/60 - Commande des caméras ou des modules de caméras
G01S 13/00 - Systèmes utilisant la réflexion ou la reradiation d'ondes radio, p. ex. systèmes radarSystèmes analogues utilisant la réflexion ou la reradiation d'ondes dont la nature ou la longueur d'onde sont sans importance ou non spécifiées
A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.
A63B 71/06 - Dispositifs indicateurs ou de marque pour jeux ou joueurs
A63B 24/00 - Commandes électriques ou électroniques pour les appareils d'exercice des groupes
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
A golf launching monitoring arrangement allows equipment to be placed at a position behind the player (i.e., behind the golf ball), to measure both club and ball movement. A 3D scan of the club head before the play serves two purposes: 1) 3D registration that enables accurate measurement of the club head position and orientation for the camera system measuring the club movement from the back; 2) for reconstruction of the launching scene. With a 3D model of the club head, a simple 3D model of the golf ball and accurate measurement of their movement during the play, a full 3D golf launching scene can be reconstructed authentically. With this reconstruction, the movement of both the club head and the resulting ball movement can be replayed at any viewing angle, with any frame rate and at whatever resolution for the players or the coaches to view and analyze.
A method of object surface matching includes identifying an object in-flight in an image; identifying a feature on the object that is in a first spatial position; comparing the feature with set of template images; identifying a first template image in the set of template images that matches the feature on the object that is in the first spatial position; determining first coordinates for the first spatial position based on the first template image; identifying a second image of the object that includes the feature on the object that is in a second spatial position; identifying a second template image in the set of template images that matches the feature on the object that is in the second spatial position; determining second coordinates for the second spatial position based on the second template image; and generating a spin value for the object based on the first and second coordinates.
Operations of the present disclosure may include receiving a group of images taken by a camera over time in an environment. The operations may also include identifying a first position of an object in a target region of the environment in a first image of the group of images and identifying a second position of the object in a second image of the group of images. Additionally, the operations may include determining an estimated trajectory of the object based on the first position of the object and the second position of the object. The operations may further include, based on the estimated trajectory, estimating a ground position in the environment associated with a starting point of the estimated trajectory of the object. Additionally, the operations may include providing the ground position associated with the starting point of the estimated trajectory of the object for display in a graphical user interface.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing an online searchable database in the field of sports performance statistics for recruiting purposes, namely, sports recruiting services for youth, high school, collegiate, and professional athletes Software as a service (SAAS) featuring software for measuring, evaluating, tracking and improving sports performance skills
A method including detecting an object within a field of view of a radar using a radar signal; tracking movement of the object through the field of view of the radar; triggering a camera to capture a plurality of images of the object based on the movement of the object; detecting the object in the plurality of images; combining data of the radar signal with data of the camera to estimate a position of the object; identifying a radar signal track generated by the motion of the object based on the combined data; and estimating a trajectory of the object based on identifying the radar signal track.
G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/254 - Analyse du mouvement impliquant de la soustraction d’images
G06T 7/277 - Analyse du mouvement impliquant des approches stochastiques, p. ex. utilisant des filtres de Kalman
G01S 13/00 - Systèmes utilisant la réflexion ou la reradiation d'ondes radio, p. ex. systèmes radarSystèmes analogues utilisant la réflexion ou la reradiation d'ondes dont la nature ou la longueur d'onde sont sans importance ou non spécifiées
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable application software for storing, editing and sharing videos among users; downloadable application software for assigning, sharing, managing and tracking tasks assigned among users; downloadable application software for storing, sharing, tracking and managing schedules among users; downloadable application software for storing, maintaining, tracking and comparison of data among users Providing temporary use of non-downloadable software for storing, editing and sharing videos among users; providing temporary use of non-downloadable software for assigning, sharing, managing and tracking tasks assigned among users; providing temporary use of non-downloadable software for storing, sharing, tracking and managing schedules among users; providing temporary use of non-downloadable software for storing, maintaining, tracking and comparison of data among users
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, downloadable mobile phone application software, USB cables, portable stand for mobile phones, carrying case and user manuals, all sold as a unit, for video recording and storage, video playback, tracking gps location information, measuring, analyzing and storing golf ball and club performance information, namely, carry distance, ball speed and direction, club speed and direction, smash factor, ball angle, club angle and ball location
71.
Object surface matching with a template for flight parameter measurement
A method of object surface matching includes identifying an object in-flight in an image; identifying a feature on the object that is in a first spatial position; comparing the feature with set of template images; identifying a first template image in the set of template images that matches the feature on the object that is in the first spatial position; determining first coordinates for the first spatial position based on the first template image; identifying a second image of the object that includes the feature on the object that is in a second spatial position; identifying a second template image in the set of template images that matches the feature on the object that is in the second spatial position; determining second coordinates for the second spatial position based on the second template image; and generating a spin value for the object based on the first and second coordinates.
According to some embodiments, the present disclosure may relate to a method including transmitting a microwave towards a moving object and receiving a reflection of the microwave reflecting off of the moving object. The method may also include determining a speed of the moving object based on the reflection of the microwave and based on the speed of the moving object and a flight path distance of the moving object, determining an optimal photograph timeframe when the moving object is in a field of view of a camera. The method may further include automatically capturing a plurality of images during the optimal photograph timeframe.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
G01S 13/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G01S 13/88 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques
The technologies described herein relate to measuring launch parameters of a flying object, such as a golf ball or a baseball. The laser based technology enables a system that is low cost which can measure launch parameters of a ball. The launch parameters are measured and rapid feedback is provided on each ball motion event and the data of every single ball launch data is stored in the backend server. The system may include a transmitter optical subassembly (TOSA), a receiver optical subassembly (ROSA), a primary processing unit, a camera subsystem, a data processing, a feedback display unit, and a backend server.
G01P 3/38 - Dispositifs caractérisés par l'emploi de moyens optiques, p. ex. en utilisant la lumière infrarouge, visible ou ultraviolette en utilisant des moyens photographiques
G01P 3/68 - Dispositifs caractérisés par la détermination du temps mis à parcourir une distance constante en utilisant des moyens optiques, c.-à-d. en utilisant la lumière infrarouge, visible ou ultraviolette
G01S 7/48 - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe
G01S 7/481 - Caractéristiques de structure, p. ex. agencements d'éléments optiques
74.
Remote heart rate monitoring based on imaging for moving subjects
Systems and methods are disclosed to measure a PPG signal. In some embodiments, a method may include capturing a plurality of frames of a subject; tracking the position of a region of interest of the subject in each of the plurality of frames; creating a first time series signal, a second time series signal, and third time series signal corresponding with respective color channels of the plurality of frames; normalizing the first, second, and third time series signals, combining the normalized first time series signal, the normalized first time series signal, and the normalized first time series signal into a combined signal; creating a spectral signal from the combined signal; and extracting the PPG signal from the spectral signal.
A61B 5/024 - Mesure du pouls ou des pulsations cardiaques
A61B 5/00 - Mesure servant à établir un diagnostic Identification des individus
A61B 5/103 - Dispositifs de mesure pour le contrôle de la forme, du dessin, de la dimension ou du mouvement du corps ou de parties de celui-ci, à des fins de diagnostic
An example embodiment includes a method of measuring launch parameters of an object in flight. The method includes capturing images of an object in flight. A radius of the object and a center of the object are identified in each of the images. A velocity, an elevation angle, and an azimuth angle are calculated based on the radius of the object, the center of the object, and pre-measured camera alignment values. The method further includes cropping the images to a smallest square that bounds the object and flattening the images from spherical representations to Cartesian representations. The method also includes converting the Cartesian representations to polar coordinates with a range of candidate centers of rotations. Based on a fit of the polar image pair, the spin axis and spin rate are measured.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G01P 3/38 - Dispositifs caractérisés par l'emploi de moyens optiques, p. ex. en utilisant la lumière infrarouge, visible ou ultraviolette en utilisant des moyens photographiques
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06K 9/52 - Extraction d'éléments ou de caractéristiques de l'image en déduisant des propriétés mathématiques ou géométriques de l'image complète
G06T 3/00 - Transformations géométriques de l'image dans le plan de l'image
An example embodiment includes an apparatus for monitoring launch parameters of an object. The apparatus includes a transmitter optical subassembly (TOSA), a receiver optical subassembly (ROSA), a processing unit, and a camera. The TOSA includes at least one laser source configured to transmit a laser sheet along an expected flight path of an object. The ROSA is configured to receive light reflected from the object. The processing unit is configured to estimate a velocity of the object based at least partially on the received light. The camera is configured to capture one or more images of the object at a time in which the object passes through a field of view of the camera according to the estimated velocity.
The technologies described herein relate to measuring launch parameters of a flying object, such as a golf ball or a baseball. The laser based technology enables a system that is low cost which can measure launch parameters of a ball. The launch parameters are measured and rapid feedback is provided on each ball motion event and the data of every single ball launch data is stored in the backend server. The system may include a transmitter optical subassembly (TOSA), a receiver optical subassembly (ROSA), a primary processing unit, a camera subsystem, a data processing, a feedback display unit, and a backend server.
An example embodiment includes a method of measuring launch parameters of an object in flight. The method includes capturing images of an object in flight. A radius of the object and a center of the object are identified in each of the images. A velocity, an elevation angle, and an azimuth angle are calculated based on the radius of the object, the center of the object, and pre-measured camera alignment values. The method further includes cropping the images to a smallest square that bounds the object and flattening the images from spherical representations to Cartesian representations. The method also includes converting the Cartesian representations to polar coordinates with a range of candidate centers of rotations. Based on a fit of the polar image pair, the spin axis and spin rate are measured.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment, namely, digital cameras, digital photo image converters, and connecting devices for photographic equipment in the nature of cable connectors and computer software, all sold as a unit for measuring ball and club performance conditions when a golf ball is struck, which then provides ball flight data and club motion data to golfers, instructors, manufacturers or club fitters, for use in golf practice, virtual play, instruction, golf club fitting, and golf club design
09 - Appareils et instruments scientifiques et électriques
Produits et services
Portable sports performance measurement equipment, namely, computer hardware, high speed digital photographic equipment, namely, digital cameras, digital photo image converters, and connecting devices for photographic equipment in the nature of cable connectors and computer software, all sold as a unit for measuring ball and club performance conditions when a golf ball is struck, which then provides ball flight data and club motion data to golfers, instructors, manufacturers or club fitters, for use in golf practice, virtual play, instruction, golf club fitting, and golf club design
An example embodiment includes an apparatus for monitoring launch parameters of an object. The apparatus includes a transmitter optical subassembly (TOSA), a receiver optical subassembly (ROSA), a processing unit, and a camera. The TOSA includes at least one laser source configured to transmit a laser sheet along an expected flight path of an object. The ROSA is configured to receive light reflected from the object. The processing unit is configured to estimate a velocity of the object based at least partially on the received light. The camera is configured to capture one or more images of the object at a time in which the object passes through a field of view of the camera according to the estimated velocity.
G01P 3/38 - Dispositifs caractérisés par l'emploi de moyens optiques, p. ex. en utilisant la lumière infrarouge, visible ou ultraviolette en utilisant des moyens photographiques
G01P 3/68 - Dispositifs caractérisés par la détermination du temps mis à parcourir une distance constante en utilisant des moyens optiques, c.-à-d. en utilisant la lumière infrarouge, visible ou ultraviolette
G01S 17/58 - Systèmes de détermination de la vitesse ou de la trajectoireSystèmes de détermination du sens d'un mouvement
G01S 17/02 - Systèmes utilisant la réflexion d'ondes électromagnétiques autres que les ondes radio
G01S 7/48 - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe
G01S 7/481 - Caractéristiques de structure, p. ex. agencements d'éléments optiques
