Abstract: A modular tracking system is described comprising of the network of independent tracking units optionally accompanied by a LIDAR scanner and/or (one or more) elevated cameras. Tracking units are combining panoramic and zoomed cameras to imitate the working principle of the human eye. Markerless computer vision algorithms are executed directly on the units and provide feedback to motorized mirror placed in front of the zoomed camera to keep tracked objects/people in its field of view. Microphones are used to detect and localize sound events. Inference from different sensor is fused in real time to reconstruct high-level events and full skeleton representation for each participant.

Abstract: A modular tracking system is described comprising of the network of independent tracking units optionally accompanied by a LIDAR scanner and/or (one or more) elevated cameras. Tracking units are combining panoramic and zoomed cameras to imitate the working principle of the human eye. Markerless computer vision algorithms are executed directly on the units and provide feedback to motorized mirror placed in front of the zoomed camera to keep tracked objects/people in its field of view. Microphones are used to detect and localize sound events. Inference from different sensor is fused in real time to reconstruct high-level events and full skeleton representation for each participant.

Abstract: Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Abstract: Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

Abstract: The sport data tracking systems available today are based on specialized hardware to detect and track targets on the field. While effective, implementing and maintaining these systems pose a number of challenges, including high cost and need for close human monitoring. On the other hand, the sports analytics community has been exploring human computation and crowdsourcing in order to produce tracking data that is trustworthy, cheaper and more accessible. However, state-of-the-art methods require a large number of users to perform the annotation, or put too much burden into a single user. Example methods, systems and user interfaces that facilitate the creation of tracking data sequences of events (e.g., plays of baseball games) by warm-starting a manual annotation process using a vast collection of historical data are described.

Abstract: Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

Abstract: Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Abstract: Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

Abstract: Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Abstract: Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

Abstract: Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Abstract: A modular tracking system is described comprising of the network of independent tracking units optionally accompanied by a LIDAR scanner and/or (one or more) elevated cameras. Tracking units are combining panoramic and zoomed cameras to imitate the working principle of the human eye. Markerless computer vision algorithms are executed directly on the units and provide feedback to motorized mirror placed in front of the zoomed camera to keep tracked objects/people in its field of view. Microphones are used to detect and localize sound events. Inference from different sensor is fused in real time to reconstruct high-level events and full skeleton representation for each participant.

Abstract: The sport data tracking systems available today are based on specialized hardware to detect and track targets on the field. While effective, implementing and maintaining these systems pose a number of challenges, including high cost and need for close human monitoring. On the other hand, the sports analytics community has been exploring human computation and crowdsourcing in order to produce tracking data that is trustworthy, cheaper and more accessible. However, state-of-the-art methods require a large number of users to perform the annotation, or put too much burden into a single user. Example methods, systems and user interfaces that facilitate the creation of tracking data sequences of events (e.g., plays of baseball games) by warm-starting a manual annotation process using a vast collection of historical data are described.