Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling

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This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.
Publisher
IEEE
Issue Date
2018-06
Language
English
Citation

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.1813 - 1820

ISSN
2160-7508
DOI
10.1109/CVPRW.2018.00225
URI
http://hdl.handle.net/10203/274660
Appears in Collection
EE-Conference Papers(학술회의논문)
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