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Tracking Sports Players with Context-Conditioned Motion Models
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 assumptiondoi:10.1109/cvpr.2013.239 dblp:conf/cvpr/LiuCCL13 fatcat:l2kjh5y2orhcpn45duyzxvclre