Tracking Sports Players with Context-Conditioned Motion Models

Jingchen Liu, Peter Carr, Robert T. Collins, Yanxi Liu
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
We employ hierarchical data association to track players in team sports. Player movements are often complex and highly correlated with both nearby and distant players. A single model would require many degrees of freedom to represent the full motion diversity and could be difficult to use in practice. Instead, we introduce a set of Game Context Features extracted from noisy detections to describe the current state of the match, such as how the players are spatially distributed. Our assumption
more » ... that players react to the current situation in only a finite number of ways. As a result, we are able to select an appropriate simplified affinity model for each player and time instant using a random decision forest based on current track and game context features. Our context-conditioned motion models implicitly incorporate complex inter-object correlations while remaining tractable. We demonstrate significant performance improvements over existing multi-target tracking algorithms on basketball and field hockey sequences several minutes in duration and containing 10 and 20 players respectively.
doi:10.1109/cvpr.2013.239 dblp:conf/cvpr/LiuCCL13 fatcat:l2kjh5y2orhcpn45duyzxvclre