Motion Fields to Predict Play Evolution in Dynamic Sport Scenes
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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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2010-06
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