Tracking players in sports videos presents numerous challenges due to weak distinguishing features and unpredictable motion. Considerable work has been done to track players in such videos using a combination of appearance and motion modeling, mostly in continuous streams of video. However, in a broadcast sports video, having advertisements, replays and intermittent change of camera view, it becomes a challenging task to keep track of players over an entire game. In this work, we solve a novel problem of tracking over a sequence of temporally disjoint soccer videos without the use of appearance cue, using a Graph based optimization approach. Each team is represented by a graph, in which the nodes correspond to player positions and the edge weights depend on spatial inter-player distance. We use team formation to associate tracks between clips and provide an end-to-end system that is able to perform statistical and tactical analysis of the game. We also introduce a new challenging dataset of an international soccer game.
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