Semi-supervised Learning for Identifying Players from Broadcast Sports Videos with Play-by-Play Information
By Jo-Anne Ting, UBC Computer Science
Tracking and identifying players in sports videos filmed with a single moving, zooming camera has many applications, but it is also a challenging problem due to fast camera motions, unpredictable player movements, and unreliable visual features. We previously introduced a system to tackle this problem based on conditional random fields. However, that system requires a large number of labeled images for training. In our most recent work, we take advantage of weakly labeled data in the form of publicly available play-by-play information. This, together with semi-supervised learning, allows us to train an identification system with very little supervision. We also propose a more robust way to predict identities of players at test time by using a simpler model based on tracklets. Our results show that we can get better identification results by using far fewer labeled training examples; semi-supervised learning with only 150 labels in a 75000-image training set outperforms a fully-supervised model learned on a 30000-image training set

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