Object-based analysis and interpretation of human motion in sports video sequences by dynamic bayesian networks

Computer Vision and Image Understanding - Tập 92 - Trang 196-216 - 2003
Ying Luo1, Tzong-Der Wu2, Jenq-Neng Hwang1
1Information Processing Lab, Department of Electrical Engineering, University of Washington, Box 352500, Seattle, WA 98195, USA
2Multimedia Technology Laboratory, Institute for Information Industry, Taipei, 106 Taiwan, ROC

Tài liệu tham khảo

D.A. Adjeroh, M.C. Lee, Adaptive transform domain video scene analysis, IEEE Intl. Conf. Multimedia Computing and Systems 1997, Ottawa, Canada, Jun., 1997 J. Bilmes, Dynamic Bayesian multinets, The 16th Conf. on Uncertainty in Artificial Intelligence, Stanford, USA, July 2000 Boreczky, 1996, Comparison of video shot boundary detection techniques, Proc. SPIE, 2670, 170, 10.1117/12.234794 J.S. Boreczky, L.D. Wilcox, A hidden Markov model framework for video segmentation using audio and image features, ICASSP’ 1998, Seattle, WA, May 1998 Cowell, 1999 S. Fischer, R. Lienhart, W. Effelsberg, Automatic recognition of film genres, in: Proc. 3rd ACM Int. Conf. Multimedia, San Francisco, CA, Nov. 1995 Ghahramani, 1998, Learning dynamic Bayesian networks, 168 Haering, 2000, A Semantic event-detection approach and its application to detecting hunts in wildlife Video, IEEE Trans. Circuits Systems Video Technol., 10, 857, 10.1109/76.867923 J. Huang, Z. Liu, Y. Wang, Y. Chen, E.K. Wong, Integration of multimodal features for video classification based on HMM. IEEE Work. Multimedia Signal Processing, Copenhagen, Denmark, Sept. 1999 C. Kim, J.N. Hwang, Video object extraction for object-oriented applications, J. VLSI Signal Processing—Systems for Signal, Image, and Video Technology, Dec. 2000 C. Kim, J.N. Hwang, Object-based video abstraction using cluster analysis. ICIP’2001, Thessaloniki, Greece, October, 2001 Kim, 2002, Fast and automatic video object segmentation and tracking for content-based applications, IEEE Trans. Circuits Systems Video Technol., 12, 122, 10.1109/76.988659 Kim, 2002, Object-based video abstraction for video surveillance systems, IEEE Trans. Circuits Systems Video Technol., 12, 1128, 10.1109/TCSVT.2002.806813 Koprinska, 2001, Video segmentation: a survey, Signal Processing: Image Communication, 16, 477, 10.1016/S0923-5965(00)00011-4 Mittal, 2001, Achieving semantic coupling in the domain of high-dimensional video indexing application, Proc. of SPIE, 4305, 97, 10.1117/12.420931 A. Mittal, L.F. Cheong, T.S. Leung, Dynamic bayesian framework for extracting temporal structure in video. CVPR’01, Hawaii, USA Dec. 2001 S. Muller, S. Eickeler, G. Rigoll, An integrated approach to shape and color-based image retrieval of rotated objects using hidden Markov models, Int. J. Pattern Recogn. Artif. Intell. 15(1), 223–237 Murphy, 2001, The Bayes net toolbox for matlab, Computing Science and Statistics, 33 K. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, Ph.D. dissertation, UC Berkeley 2002 Naphade, 2001, A Probabilistic framework for semantic video indexing, filtering, and retrieval, IEEE Trans. Multimedia, 3, 141, 10.1109/6046.909601 Naphade, 2002, A factor graph framework for semantic video indexing, IEEE Trans. Circuits System Video Technol., 12, 40, 10.1109/76.981844 Oliver, 2000, A Bayesian computer vision system for modeling human interactions, IEEE Trans. Pattern Analysis Machine Intell., 22, 831, 10.1109/34.868684 Parker, 1997 V. Pavlovic, J.M. Rehg, T.J. Cham, K. Murphy, A dynamic Bayesian network approach to figure tracking using learned dynamic models. ICCV’99, Corfu, Greece, Sept. 1999 Rabiner, 1989, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, 77, 257, 10.1109/5.18626 Rui, 1999, Image retrieval: current techniques, promising, directions, and open issues, J. Vis. Commun. Image Represent., 10, 39, 10.1006/jvci.1999.0413 Satoh, 1999, Name-It: naming and detecting faces in news videos, IEEE Multimedia, 6, 22, 10.1109/93.752960 Starner, 1995, Visual recognition of American sign language using hidden Markov models, 189 Y. Taniquchi, A. Akutsu, Y. Tonomura, Panorama excerpts: extracting and packing panoramas for video browsing, Proc. ACM Multimedia 97. ACM, New York, USA, 1997 F. Tsutsumi, C. Nakajima, Hybrid approach of video indexing and machine learning for rapid indexing and highly precise object recognition, ICIP’2001, Thessaloniki, Greece, October, 2001 Vasconcelos, 2000, Statistical models of video structure for content analysis and characterization, IEEE Trans. Image Process., 9, 3, 10.1109/83.817595 Wang, 2000, Multimedia content analysis using audio and visual information, IEEE Signal Process. Magazine, 17, 12, 10.1109/79.888862 G. Xu, Y.F. Ma, H.J. Zhang, S. Yang, Motion based event recognition using HMM. ICPR’ 2002, Québec City, Canada, Aug. 2002 Y. Yuan, Q.B. Song, J.Y. Shen, Automatic video classification using decision tree method. IEEE Int. Conf. Machine Learn. Cybernet. 2002, Beijing, China, Nov. 2002 Zhong, 1999, An integrated approach of content-based video object segmentation and retrieval, IEEE Trans. Circuits Systems Video Technol., 9, 1259, 10.1109/76.809160 G. Zweig. Speech recognition by dynamic Bayesian networks. Ph.D. dissertation, UC Berkeley, 1998