To appear at CVPR 2010
San Francisco, CA
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Authors |
Kihwan
Kim
Georgia
Institute of Technology
Iain
Matthews
Disney
Research Pittsburgh
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Matthias
Grundmann
Georgia
Institute of Technology
Jessica
Hodgins
Disney
Research Pittsburgh
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Ariel
Shamir
The
Interdisciplinary Center
Irfan
Essa
Georgia
Institute of Technology
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Abstract |
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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Paper |
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Video |
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Citation |
@inproceedings{Kim:2010a:motion-field, 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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Slides |
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Funding |
This research is supported by:
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Media Coverage / Links to similar projects |
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Copyright |
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