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

Computer Vision and Image Understanding

Volumn 92, Issue 2-3, 2003, Pages 196-216

  Luo, Ying ^a   Wu, Tzong Der ^b   Hwang, Jenq Neng ^a  

^a University of Washington   (United States)

^b INSTITUTE FOR INFORMATION INDUSTRY   (Taiwan)

Author keywords

Dynamic Bayesian network (DBN); Hidden Markov model (HMM); MPEG 7; Video abstraction; Video object (VO)

Indexed keywords

ABSTRACTING; ALGORITHMS; DATA REDUCTION; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; MARKOV PROCESSES; NEURAL NETWORKS; OBJECT RECOGNITION; REDUNDANCY; SEMANTICS; VIDEO RECORDING;

DYNAMIC BAYESIAN NETWORKS; HIDDEN MARKOV MODEL; HUMAN MOTION INTERPRETATION; MOTION PICTURES EXPERTS GROUP; OBJECT BASED ANALYSIS; SEMANTIC EVENT MODELING; SPORTS VIDEO SEQUENCE; VIDEO OBJECT ABSTRACTION; VIDEO OBJECT EXTRACTION;

MOTION COMPENSATION;

EID: 0345492459     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cviu.2003.08.001     Document Type: Article

References (36)

1. D.A. Adjeroh, M.C. Lee, Adaptive transform domain video scene analysis, IEEE Intl. Conf. Multimedia Computing and Systems 1997, Ottawa, Canada, Jun., 1997.
2. J. Bilmes, Dynamic Bayesian multinets, The 16th Conf. on Uncertainty in Artificial Intelligence, Stanford, USA, July 2000.
3. J.S. Boreczky, L.A. Rowe, Comparison of video shot boundary detection techniques, Proc. SPIE 2670 (1996) 170-179.
4. 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.
5. R.G. Cowell, A.P. Dawid, S.L. Lauritzen, D.J. Spiegelhalter, Probabilistic Networks and Expert Systems, Springer-Verlag, Berlin-Heidelberg-New York, 1999.
6. S. Fischer, R. Lienhart, W. Effelsberg, Automatic recognition of film genres, in: Proc. 3rd ACM Int. Conf. Multimedia, San Francisco, CA, Nov. 1995.
7. Z. Ghahramani, Learning dynamic Bayesian networks, in: Adaptive Processing of Sequences and Data Structures, Springer-Verlag, Berlin-Heidelberg-New York, 1998, pp. 168-197.
8. N. Haering, R.J. Qian, M.I. Sezan, A Semantic event-detection approach and its application to detecting hunts in wildlife Video, IEEE Trans. Circuits Systems Video Technol. 10 (6) (2000) 857-867.
9. 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.
10. 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.
11. C. Kim, J.N. Hwang, Object-based video abstraction using cluster analysis. ICIP'2001, Thessaloniki, Greece, October, 2001.
12. C. Kim, J.N. Hwang, Fast and automatic video object segmentation and tracking for content-based applications, IEEE Trans. Circuits Systems Video Technol. 12 (2) (2002) 122-129.
13. C. Kim, J.N. Hwang, Object-based video abstraction for video surveillance systems, IEEE Trans. Circuits Systems Video Technol. 12 (12) (2002) 1128-1138.
14. I. Koprinska, S. Carrato, Video segmentation: a survey, Signal Processing: Image Communication 16 (5) (2001) 477-500.
15. A. Mittal, L.F. Cheong, Achieving semantic coupling in the domain of high-dimensional video indexing application, Proc. of SPIE 4305 (2001) 97-107.
16. A. Mittal, L.F. Cheong, T.S. Leung, Dynamic bayesian framework for extracting temporal structure in video. CVPR'01, Hawaii, USA Dec. 2001.
17. 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.
18. K. Murphy, The Bayes net toolbox for matlab, Computing Science and Statistics 33 (2001).
19. K. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, Ph.D. dissertation, UC Berkeley 2002.
20. M.R. Naphade, T.S. Huang, A Probabilistic framework for semantic video indexing, filtering, and retrieval, IEEE Trans. Multimedia 3 (1) (2001) 141-151.
21. M.R. Naphade, I.V. Kozintsev, T.S. Huang, A factor graph framework for semantic video indexing, IEEE Trans. Circuits System Video Technol. 12 (1) (2002) 40-52.
22. N.M. Oliver, B. Rosario, A.P. Pentland, A Bayesian computer vision system for modeling human interactions, IEEE Trans. Pattern Analysis Machine Intell. 22 (8) (2000) 831-843.
23. J.R. Parker, Algorithms for Image Processing and Computer Vision, Wiley Computer Publishing, 1997.
24. 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.
25. L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (2) (1989) 257-286.
26. Y. Rui, T.S. Huang, Image retrieval: current techniques, promising, directions, and open issues, J. Vis. Commun. Image Represent. 10 (1999) 39-62.
27. S. Satoh, Y. Nakamura, T. Kanade, Name-It: naming and detecting faces in news videos, IEEE Multimedia 6 (1) (1999) 22-35.
28. T. Starner, A. Pentland, Visual recognition of American sign language using hidden Markov models, in: Int. Work. Automatic Face Gesture Recogn., Zurich, Switzerland, 1995, pp. 189-194.
29. Y. Taniquchi, A. Akutsu, Y. Tonomura, Panorama excerpts: extracting and packing panoramas for video browsing, Proc. ACM Multimedia 97. ACM, New York, USA, 1997.
30. 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.
31. N. Vasconcelos, A. Lippman, Statistical models of video structure for content analysis and characterization, IEEE Trans. Image Process. 9 (1) (2000) 3-19.
32. Y. Wang, Z. Liu, J. Huang, Multimedia content analysis using audio and visual information, IEEE Signal Process. Magazine 17 (6) (2000) 12-36.
33. G. Xu, Y.F. Ma, H.J. Zhang, S. Yang, Motion based event recognition using HMM. ICPR' 2002, Québec City, Canada, Aug. 2002.
34. 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.
35. D. Zhong, S.F. Chang, An integrated approach of content-based video object segmentation and retrieval, IEEE Trans. Circuits Systems Video Technol. 9 (8) (1999) 1259-1268.
36. G. Zweig. Speech recognition by dynamic Bayesian networks. Ph.D. dissertation, UC Berkeley, 1998.