Contrastive Learning for Sports Video: Unsupervised Player Classification

• URL: http://arxiv.org/abs/2104.10068v1
• Date: Thu, 15 Apr 2021 20:24:02 GMT
• Title: Contrastive Learning for Sports Video: Unsupervised Player Classification
• Authors: Maria Koshkina, Hemanth Pidaparthy, James H. Elder
• Abstract summary: We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams. We show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time)
• Score: 2.3633885460047765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

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