Identifying players in broadcast sports videos using conditional random fields

We are interested in the problem of automatic tracking and identification of players in broadcast sport videos shot with a moving camera from a medium distance. While there are many good tracking systems, there are fewer methods that can identify the tracked players. Player identification is challen...

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Bibliographic Details
Published inCVPR 2011 pp. 3249 - 3256
Main Authors Wei-Lwun Lu, Ting, Jo-Anne, Murphy, K. P., Little, J. J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Cameras
Detectors
Feature extraction
Image color analysis
Tracking
Videos
Visualization
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Summary:We are interested in the problem of automatic tracking and identification of players in broadcast sport videos shot with a moving camera from a medium distance. While there are many good tracking systems, there are fewer methods that can identify the tracked players. Player identification is challenging in such videos due to blurry facial features (due to fast camera motion and low-resolution) and rarely visible jersey numbers (which, when visible, are deformed due to player movements). We introduce a new system consisting of three components: a robust tracking system, a robust person identification system, and a conditional random field (CRF) model that can perform joint probabilistic inference about the player identities. The resulting system is able to achieve a player recognition accuracy up to 85% on unlabeled NBA basketball clips.
ISBN:1457703947
9781457703942
ISSN:1063-6919
DOI:10.1109/CVPR.2011.5995562