Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Systems and methods are described for detecting a triggering event in a gaming environment and recording a segment of video. In some aspects, the method may include obtaining a camera projection matrix associated with the gaming environment that correlates points in the gaming environment between a two-dimensional pixel space and a three-dimensional physical representation. An input video may be obtained from the gaming environment. Pose coordinates, including two-dimensional pixel space location, of a body part of a person in the input video may be estimated, and converted to three-dimensional pose coordinates using the camera projection matrix. A person of interest having a body part located within a region of interest in the gaming environment may be identified to determine if a triggering event has occurred. Storage of an input video segment that includes the trigger movement may be initiated on a storage device in communication with the user device.

Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Systems and methods are described for generating a three-dimensional track a ball in a gaming environment from a single camera. In some examples, an input video including frames of a ball moving in a gaming environment recorded by a camera may be obtained, along with a camera projection matrix associated with at least one frame that maps a two-dimensional pixel space representation to a three-dimensional representation of the gaming environment. Candidate two-dimensional image locations of the ball across the plurality of frames may be identified using a neural network or a computer vision algorithm. An optimization algorithm may be performed that uses a 3D ball physics model, the camera projection matrix and a subset of the candidate two-dimensional image locations of the ball to generate a three-dimensional track of the ball in the gaming environment. The three-dimensional track of the ball may then be provided to a user device.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Systems and methods are described for generating a three-dimensional track of a ball in a gaming environment from multiple cameras. In some examples, at least two input videos, each including frames of a ball moving in a gaming environment recorded by a camera, may be obtained, along with a camera projection matrix that maps a two-dimensional pixel space representation to a three-dimensional representation of the gaming environment. Candidate two-dimensional image locations of the ball across the plurality of frames of the at least two input videos may be identified using neural network or computer vision techniques. An optimization algorithm may be performed that uses a 3D ball physics model, the camera projection matrix and a subset of the candidate two-dimensional image locations of the ball from the at least two input videos to generate a three-dimensional track of the ball in the gaming environment.

Abstract: Systems and methods are described for generating a three-dimensional track a ball in a gaming environment from a single camera. In some examples, an input video including frames of a ball moving in a gaming environment recorded by a camera may be obtained, along with a camera projection matrix associated with at least one frame that maps a two-dimensional pixel space representation to a three-dimensional representation of the gaming environment. Candidate two-dimensional image locations of the ball across the plurality of frames may be identified using a neural network or a computer vision algorithm. An optimization algorithm may be performed that uses a 3D ball physics model, the camera projection matrix and a subset of the candidate two-dimensional image locations of the ball to generate a three-dimensional track of the ball in the gaming environment. The three-dimensional track of the ball may then be provided to a user device.

Abstract: Systems and methods are described for detecting a triggering event in a gaming environment and recording a segment of video. In some aspects, the method may include obtaining a camera projection matrix associated with the gaming environment that correlates points in the gaming environment between a two-dimensional pixel space and a three-dimensional physical representation. An input video may be obtained from the gaming environment. Pose coordinates, including two-dimensional pixel space location, of a body part of a person in the input video may be estimated, and converted to three-dimensional pose coordinates using the camera projection matrix. A person of interest having a body part located within a region of interest in the gaming environment may be identified to determine if a triggering event has occurred. Storage of an input video segment that includes the trigger movement may be initiated on a storage device in communication with the user device.

Abstract: Systems and methods are described for generating a three-dimensional track a ball in a gaming environment from a single camera. In some examples, an input video including frames of a ball moving in a gaming environment recorded by a camera may be obtained, along with a camera projection matrix associated with at least one frame that maps a two-dimensional pixel space representation to a three-dimensional representation of the gaming environment. Candidate two-dimensional image locations of the ball across the plurality of frames may be identified using a neural network or a computer vision algorithm. An optimization algorithm may be performed that uses a 3D ball physics model, the camera projection matrix and a subset of the candidate two-dimensional image locations of the ball to generate a three-dimensional track of the ball in the gaming environment. The three-dimensional track of the ball may then be provided to a user device.

Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Methods and systems for finding and ranking video sequences that contain tactical sequences (e.g., an uninterrupted portion of a sporting event) that are similar to tactical event content in a target video sequence, such as those occurring in sports, include learning and labelling (e.g., through use of metadata and the like) tactically significant sequences in a plurality of video segments, including a plurality of such tactically significant sequences in a single video segment.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.

Abstract: Data processing systems are disclosed for determining semantic and person keypoints for an environment and an image and matching the keypoints for the image to the keypoints for the environment. A homography is generated based on the keypoint matching and decomposed into a matrix. Camera parameters are then determined from the matrix. A plurality of random camera poses can be generated and used to project keypoints for an environment using image keypoints. The projected keypoints can be compared to the actual keypoints for the environment to determine an error and weighting for each of the random camera poses.