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Sports video analysis is a common tool in team sports. It requires keeping track of player positions with corresponding identities during the game and analyzing the resulting trajectories at an abstract level. This thesis proposes a distributed cognitive system for automating this task. The tracking process includes information fusion and building as well as adapting models of the tracked players online; the system supports automated team behavior summarization and offers further analysis in a conceptualization framework. Our contributions are (1) an innovative, general multi-target tracking approach that outperforms current state-of-the-art algorithms, (2) adaptive methods for identifying players based on appearance and/or spatial relations with the help of extended self-organizing neural networks, and (3) the implementation of a concrete real-time tracking system for recorded soccer games. Research results are validated in various challenging domains, including full-length soccer match videos and broadcasted material.

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graduated in Informatics and received a PhD in this field from the Technische Universität München, Germany. He worked in the Intelligent Autonomous Systems group of Prof. Michael Beetz, PhD. His research focuses on automated sports analysis with a strong interest in online learning and knowledge extraction.

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