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Abstract: We propose an original bayesian approach to recognize human behaviors from video streams. Mobile objects and their visual features are computed by a vision module. Then, using a Recurrent Bayesian Network, behaviors of the mobile objects are recognized through the temporal evolution of their visual features. (Update)
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BibTeX entry: (Update)
Nicolas Moenne-Loccoz, Francois Bremond, and Monique Thonnat. Recurrent bayesian network for the recognition of human behaviors from video. In 3th International Conference On Computer Vision Systems, 2003. http://citeseer.ist.psu.edu/720846.html More
@misc{ moenne-loccoz03recurrent,
author = "N. Moenne-Loccoz and F. Bremond and M. Thonnat",
title = "Recurrent bayesian network for the recognition of human behaviors from
video",
text = "Nicolas Moenne-Loccoz, Francois Bremond, and Monique Thonnat. Recurrent
bayesian network for the recognition of human behaviors from video. In 3th
International Conference On Computer Vision Systems, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/720846.html" }
Citations (may not include all citations):92 Coupled hidden markov models for complex action recognition - Brand, Oliver et al. - 1997
32 Advanced Visual Surveillance using Bayesian Networks - Buxton, Gong - 1995
21 Action recognition using probabilistic parsing - Bobick, Ivanov - 1998
14 Bayesian framework for video surveillance application - Hongeng, Br et al. - 2000
12 Representation and optimal recognition of human activities - Hongeng, Br et al. - 2000
11 Image understanding for visual surveillance application (context) - Thonnat, Rota - 1999
7 Hierarchical unsupervised learning of facial expression cate.. (context) - Hoey - 2001
5 Learning behavior models of human activities (context) - Galata, Johnson et al. - 1999
3 Recognizing simple behaviors using time-delay rbf network (context) - Howell, Buxton - 1997
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