Motion Field to Predict Play Evolution in Dynamic Sport Scenes  

To appear at CVPR 2010 San Francisco, CA


Kihwan Kim

Georgia Institute of Technology

Iain Matthews

Disney Research Pittsburgh

Matthias Grundmann

Georgia Institute of Technology

Jessica Hodgins

Disney Research Pittsburgh

Ariel Shamir

The Interdisciplinary Center

Irfan Essa

Georgia Institute of Technology


Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game. We show that constrained multi-agent events can be analyzed and even predicted from video. Such analysis requires estimating the global movements of all players in the scene at any time, and is needed for modeling and predicting how the multi-agent play evolves over time on the field. To this end, we propose a novel approach to detect the locations of where the play evolution will proceed, e.g. where interesting events will occur, by tracking player positions and movements over time. We start by extracting the ground level sparse movement of players in each time-step, and then generate a dense motion field. Using this field we detect locations where the motion converges, implying positions towards which the play is evolving. We evaluate our approach by analyzing videos of a variety of complex soccer plays.


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       Author = {Kihwan Kim, Matthias Grundmann, Ariel Shamir, Iain Matthews, Jessica Hodgins and Irfan Essa},
       Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
       Month = {June},
       Organization = {IEEE Computer Society},
       Title = {Motion Field to Predict Play Evolution In Dynamic Sport Scenes},
       Year = {2010},

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