Automatic detection and recognition of athlete actions in diving video

Authors:
Haojie Li;Si Wu;Shan Ba;Shouxun Lin;Yongdong Zhang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;SAMI Lab, France Telecom R&D Beijing, Beijing, China;School of Science, Beijing Institute of Technology, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Year:
2007

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Abstract

This paper presents a system for automatic detecting and recognizing complex individual actions in sports video to facilitate high-level content-based video indexing and retrieval. This is challenging due to the cluttered and dynamic background in sports video which makes object segmentation formidable. Another difficulty is to fully automatically and accurately detect desired actions from long video sequence. We propose three techniques to handle these challenges. Firstly, an efficient approach exploiting dominant motion and semantic color analysis is developed to detecting the highlight clips which contain athlete’s action from video sequences. Secondly, a robust object segmentation algorithm based on adaptive dynamic background construction is proposed to segment the athlete’s body from the clip. Finally, to recognize the segmented body shape sequences, the hidden markov models are slightly modified to make them suitable for noisy data processing. The proposed system for broadcast diving video analysis has achieved 96.6% detection precision; and 85% recognition accuracy for 13 kinds of diving actions.