ABSTRACT
Recognition of player actions in broadcast sports video is a challenging task due to low resolution of the players in video frames. In this paper, we present a novel method to recognize the basic player actions in broadcast tennis video. Different from the existing appearance-based approaches, our method is based on motion analysis and considers the relationship between the movements of different body parts and the regions in the image plane. A novel motion descriptor is proposed and supervised learning is employed to train the action classifier. We also propose a novel framework by combining the player action recognition with other multimodal features for semantic and tactic analysis of the broadcast tennis video. Incorporating action recognition into the framework not only improves the semantic indexing and retrieval performance of the video content, but also conducts highlights ranking and tactics analysis in tennis matches, which is the first solution to our knowledge for tennis game. The experimental results demonstrate that our player action recognition method outperforms existing appearance-based approaches and the multimodal framework is effective for broadcast tennis video analysis.
- Y. Gong, T.S. Lim, H.C. Chua, H.J. Zhang, M. Sakauchi. Automatic parsing of TV soccer programs. IEEE International Conference on Multimedia Computing and System, pp. 167--174, 1995. Google ScholarDigital Library
- A. Ekin, A.M. Tekalp, R. Mehrotra. Automatic soccer video analysis and summarization. IEEE Transaction on Image Processing, vol. 12, no. 7, pp. 796--807, 2003. Google ScholarDigital Library
- L. Xie, P. Xu, S.F. Chang, A. Divakaran, H. Sun. Structure analysis of soccer video with domain knowledge and hidden Markov models. Pattern Recognition Letters, vol. 25, no. 7, pp. 767--775, 2004. Google ScholarDigital Library
- J. Assfalg, M. Bertini, C. Colombo, A. Delbimbo, W. Nunziati. Semantic annotation of soccer video: automatic highlights identification. Computer Vision and Image Understanding, vol. 92, no. 2-3, pp. 285--305, 2003. Google ScholarDigital Library
- N. Babaguchi, Y. Kawai, T. Ogura, T. Kitahashi. Personalized abstraction of broadcasted American football video by highlight selection. IEEE Transaction on Multimedia, vol. 6, no. 4, pp. 575--586, 2004. Google ScholarDigital Library
- K. Wan, J. Wang, C. Xu, Q. Tian. Automatic sports highlights extraction with content augmentation. Pacific-Rim Conference on Multimedia, vol. 3332, pp. 19--26, 2004. Google ScholarDigital Library
- G. S. Pingali, Y. Jean, A. Opalach, I. Carlbom. LucentVision: converting real world events into multimedia experiences. IEEE International Conference on Multimedia and Expo, vol. 3, pp. 1433--1436, 2000.Google ScholarCross Ref
- M. Shah, R. Jain. Motion-based Recognition. Computational Image and Vision Series. Kluwer Academic Publishers, 1997. Google ScholarDigital Library
- D.M. Gavrila. The visual analysis of human movement: a survey. Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82--98, 1999. Google ScholarDigital Library
- R. Cutler, L.S. Davis. Robust real-time periodic motion detection, analysis, and application. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 781--796, 2000. Google ScholarDigital Library
- A.A. Efros, A.C. Berg, G. Mori, J. Malik. Recognizing action at a distance. IEEE International Conference on Computer Vision, vol. 2, pp. 726--733, 2003. Google ScholarDigital Library
- A.F. Bobick, J.W. Davis. The recognition of human movement using temporal templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 23, no. 3, pp. 257--267, 2001 Google ScholarDigital Library
- Y. Song, L. Goncalves, P. Perona. Unsupervised learning of human motion. IEEE Transaction of Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 814--827, 2003. Google ScholarDigital Library
- H. Miyamori, S. Iisaku. Video annotation for content-based retrieval using human behavior analysis and domain knowledge. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 320--325, 2000. Google ScholarDigital Library
- H. Miyamori. Improving accuracy in behavior identification for content-based retrieval by using audio and video information. IEEE International Conference on Pattern Recognition, vol. 2, pp. 826--830, 2002.Google Scholar
- G. Sudhir, J.C.M. Lee, A.K. Jain. Automatic classification of tennis video for high-level content-based retrieval. IEEE International Workshop on Content-Based Access of Image and Video Databases, pp. 81--90, 1998. Google ScholarDigital Library
- G.S. Pingali, Y. Jean, I. Carlbom. Real time tracking for enhanced tennis broadcasts. IEEE Conference on Computer Vision and Pattern Recognition, pp. 260--265, 1998. Google ScholarDigital Library
- X. Yu, C.H. Sim, J.R. Wang, L.F. Cheong. A trajectory-based ball detection and tracking algorithm in broadcast tennis video. IEEE International Conference on Image Processing, vol. 2, pp. 1049--1052, 2004.Google Scholar
- E. Kijak, G. Gravier, P. Gros, L. Oisel, F. Bimbot. HMM based structuring of tennis videos using visual and audio cues. IEEE International Conference on Multimedia and Expo, vol. 3, pp. 309--312, 2003. Google ScholarDigital Library
- M. Xu, L.Y. Duan, C.S. Xu, Q. Tian. A fusion scheme of visual and auditory modalities for event detection in sports video. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 189--192, 2003.Google Scholar
- M. Petkovic, V. Mihajlovic, W. Jonker. Techniques for automatic video content derivation. IEEE International Conference on Image Processing, vol. 2, pp. 611--614, 2003.Google ScholarCross Ref
- P. Wang, R. Cai, S.Q. Yang. A tennis video indexing approach through pattern discovery in interactive process. Pacific-Rim Conference on Multimedia, vol. 3331, pp. 49--56, 2004. Google ScholarDigital Library
- L. Xing, H. Yu, Q. Huang, Q. Ye, A. Divakaran. Subjective evaluation criterion for selecting affective features and modeling highlights. SPIE Conference on Multimedia Content Analysis and Management, vol. 6073, 2006.Google ScholarCross Ref
- G. Zhu, D. Liang, Y. Liu, Q. Huang, W. Gao. Improving particle filter with support vector regression for efficient visual tracking. IEEE International Conference on Image Processing, vol. 2, pp. 422--425, 2005.Google Scholar
- B.K.P. Horn, B.G. Schunck. Determining optical flow. Artificial Intelligence, vol. 17, pp. 185--203, 1981.Google ScholarDigital Library
- S. Jiang, Q. Ye, W. Gao, T. Huang. A new method to segment playfield and its applications in match analysis in sports video. ACM Multimedia, pp. 292--295, 2004. Google ScholarDigital Library
- Q. Ye, W. Gao, W. Zeng. Color image segmentation using density-based clustering. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 345--348, 2003.Google Scholar
- D. Comaniciu, V. Ramesh, P. Meer. Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564--577, 2003. Google ScholarDigital Library
- V. Vapnik. The nature of statistical learning theory. Springer-Verlag, New York, 1995. Google ScholarDigital Library
- R. Hartley, A. Zisserman. Multiple view geometry in computer vision, Cambridge University Press 2003, UK. Google ScholarDigital Library
- H. Liu, D. Zhou. Content-based news video story segmentation and video retrieval. SPIE Conference on Image and Graphics, vol. 4875, pp. 1038--1044, 2002.Google ScholarCross Ref
Index Terms
- Player action recognition in broadcast tennis video with applications to semantic analysis of sports game
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