Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video
This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.
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[1] URL of the website of the SIMILAR European Network of Excellence,
"http://www.similar.cc/," .
[2] J. K. Aggarwal and Q. Cai, "Human motion analysis: A review," Computer
Vision and Image Understanding, vol. 73, no. 3, pp. 428-440, 1999.
[3] J. J. Wang and S. Singh, "Video analysis of human dynamics- a survey,"
Real-Time Imaging, vol. 9, pp. 321-346, 2003.
[4] L. Wang, W. Hu, and T. Tan, "Recent developments in human motion
analysis," Pattern Recognition, vol. 36, no. 3, pp. 585-601, 2003.
[5] C. W. Sul, K. C. Lee, and K. Wohn, "Virtual stage: a location-based
karaoke system," IEEE Multimedia, vol. 5, no. 2, pp. 42-52, 1998.
[6] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, "Pedestrian
detection using wavelet templates," in Computer Vision and Pattern
Recognition, 1997, pp. 193-199.
[7] I. Haritaoglu, D. Harwood, and L. Davis, "Ghost: A human body part
labeling system using silhouettes," in International Conference on Computer
Vision and Pattern Recognition, 1998, pp. 77-82.
[8] L. Campbell and A. Bobick, "Using phase space constraints to represent
human body motion," International Workshop on Automatic Face and
Gesture Recognition, 1995.
[9] G. Shafer, "A mathematical theory of evidence," Princeton University
Press, 1976.
[10] A. Dempster, "A generalization of bayesian inference," Journal of the
Royal Statistical Society, vol. 30, pp. 205-245, 1968.
[11] P. Smets and R. Kennes, "The transferable belief model," Artificial Intelligence,
vol. 66, pp. 191-234, 1994.
[12] P. Smets, "The transferable belief model for quantified belief representation,"
in Handbook of Defeasible Reasoning and Uncertainty Management
Systems, Vol. 1, D. M. Gabbay and P. Smets, Eds., pp. 267-301.
Kluwer, Doordrecht, The Netherlands, 1998.
[13] A. Caplier, L. Bonnaud, and J-M. Chassery, "Robust fast extraction of
video objects combining frame differences and adaptative reference image,"
in IEEE International Conference on Image Processing, September
2001.
[14] V. Girondel, L. Bonnaud, and A. Caplier, "Hands detection and tracking
for interactive multimedia applications," in International Conference on
Computer Vision and Graphics, September 2002, pp. 282-287.
@article{"International Journal of Information, Control and Computer Sciences:53020", author = "V. Girondel and L. Bonnaud and A. Caplier and M. Rombaut", title = "Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video", abstract = "This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.
", keywords = "Belief theory, classifiers comparison, data fusion, human motion analysis, real-time processing, static posture recognition.", volume = "1", number = "4", pages = "908-4", }
