State filtering and change detection using TBM conflict application to human action recognition in athletics videos

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 17, Issue 7, 2007, Pages 944-949

  Ramasso, Emmanuel ^a   Rombaut, Michèle ^a   Pellerin, Denis ^a  

^a GIPSA LAB   (France)

Author keywords

Belief state filtering; Human motion analysis; Moving camera; Novelty detection; Transferable belief model

Indexed keywords

FUNCTION EVALUATION; MARKOV PROCESSES; MATHEMATICAL MODELS; MOTION PICTURES; REAL TIME SYSTEMS; VIDEO RECORDING;

BELIEF STATE FILTERING; HUMAN MOTION ANALYSIS; MOVING CAMERA; NOVELTY DETECTION; TRANSFERABLE BELIEF MODELS;

GESTURE RECOGNITION;

EID: 34547442584     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2007.896652     Document Type: Article

References (19)

1. T. Moeslund and E. Granum, "A survey of computer vision-based human motion capture," Comput. Vis. Image Understand, vol. 81, pp. 231-268, 2001.
2. S. Hongeng, R. Nevatia, and F. Bremond, "Video-based event recognition and probabilistic recognition methods," Comput. Vis. Image Understand., vol. 96, pp. 129-162, 2004.
3. P. Smets and R. Kennes, "The transferable belief model," Artif. Intel!., vol. 66, pp. 191-234, 1994.
4. G. Klir and M. Wierman, Uncertainty-Based Information. Elements of Generalized Information Theory, 2nd ed. New York: Springer-Verlag, 1999.
5. P. Smets, "What is dempster-shafer's model?," Adv. Dempster-Shafer Theory of Evidence, pp. 5-34, 1994.
6. V. Girondel, A. Caplier, L. Bonnaud, and M. Rombaut, "Belief theory-based classifiers comparison for static human body postures recognition in video," Signal Process., vol. 2, pp. 29-33, 2005.
7. Z. Hammal, A. Caplier, and M. Rombaut, "Belief theory applied to facial expressions classification," in Proc. Int. Conf. Adv. Pattern Recognit., Bath, U.K., 2005, pp. 183-191.
8. E. Ramasso, M. Rombaut, and D. Pellerin, "A temporal belief filter improving human action recognition in videos," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, vol. 2, pp. 141-144.
9. E. Ramasso, D. Pellerin, C. Panagiotakis, M. Rombaut, G. Tziritas, and W. Lim, "Spatio-temporal information fusion for human action recognition in videos," presented at the Eur. Signal Process. Conf., Antalya, Turkey, Sep. 2005.
10. L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-285, Feb. 1989.
11. A. Efros, A. Berg, G. Mori, and J. Malik, "Recognizing action at a distance," in Proc. IEEE ICCV, 2003, pp. 726-733.
12. K. P. Murphy, "Dynamic Bayesian networks: Representation, inference and learning," Ph.D. dissertation, Comp. Sci. Dept., Univ. Calif. Berkeley, CA, 2002.
13. T. Xiang and S. Gong, "Discovering Bayesian causality among visual events in a complex outdoor scene," in Proc. IEEE AVSS, 2003, pp. 177-182.
14. Y. Luo, T. Wu, and J. Hwang, "Object-based analysis and interpretation of human motion in sports video sequences by dynamic Bayesian networks," Comput. Vis. Image Understand., vol. 92, pp. 196-216, 2003.
15. A. Wilson and A. Bobick, "Hidden Markov models for modeling and recognizing gesture under variation," Int. J. Pattern Recognit, Artif. Intell., vol. 15, pp. 123-160, 2001.
16. M. Shah, "Understanding human behavior from motion imagery," Mach. Vis. Appli., vol. 14, pp. 210-214, 2003.
17. F. Delmotte and P. Smets, "Target identification based on the transferable belief model interpretation of Dempster-Shafer model," IEEE Trans. Syst. Man Cybern., vol. 34, no. 4, pp. 457-471, Jul. 2004.
18. P. Smets, "Beliefs functions: The disjunctive rule of combination and the generalized Bayesian theorem," Int. J. Approximate Reas., vol. 9, pp. 1-35, 1993.
19. M. Figueiredo and A. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 381-396, Mar. 2002.