Disney Enterprises, Inc.

Qualiaos, Inc.

Kevin John Prince

Dirk Edward Van Dall

Justin Ali Kennedy

Carlos Augusto Dietrich

Keith Comito

Carlos Dietrich

Systems and techniques are provided to identify, analyze, and evaluate key events and mechanical variables in videos of human motion related to an action, such as may be used in training for various sports and other activities. Information about the action is calculated based on analysis of the video such as via keypoint identification, pose identification and/or estimation, and related calculations, and provided automatically to the user to allow for improvement of the action.

Detection of kinetic events and mechanical variables from uncalibrated video

A system for performing synergistic object tracking and pattern recognition for event representation includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive event data corresponding to one or more propertie(s) of an object, to generate, using the event data, a location data estimating a location of each of multiple predetermined landmarks of the object, and to predict, using one or both of the event data and the location data, a pattern corresponding to the propertie(s) of the object. The processing hardware is further configured to execute the software code to update, using the predicted pattern, the location data, and to merge the updated location data and the predicted pattern to provide merged data.

Synergistic object tracking and pattern recognition for event representation

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.

Detection of contacts among event participants

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.

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Brooklyn, NY, United States of America

Kevin John Prince