Changing the Game: Novel Computer Vision Algorithms for Automated Sports Analytics

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We aim for a comprehensive annotation of human activity in sports video data. Our long-term goal is automatic methods that can determine actions for every person at every time instant, label interactions between players, and classify social roles and higher-level game situations. We will develop novel models and learning algorithms towards this long-term goal. Our short-term objectives are to develop deep structured learning frameworks capable of handling varied structural inputs in domains including tracking and high-level activity analysis. We will learn complex deep representations for the tasks of player detection, tracking, and activity recognition including atomic actions perform by each player as well as player-player relationships. Further, our goal is to develop online, resource-bounded tracking algorithms. Learning independent representations for each task is sub-optimal unless one has access to very large training sets for each tasks. To reduce the required amount of labeled data, we proposed to develop new learning algorithms that can learn deep representations while taking into account the dependencies of all these tasks. This will allow each task to leverage the supervision provided to the other tasks. The resulting algorithms and learning methods will advance not only the rapidly growing sports analytics industry, both for professional and amateur sports, but will also contributed to progress in human activity understanding from video.