Probabilistic Motion Parameter Models for Human Activity Recognition

Xinding Sun, Ching-Wei Chen, B. S. Manjunath

Dept. of Electrical and Computer Engineering
University of California at Santa Barbara,
Santa Barbara, CA 93106
E-mail: {xdsun, cwei, manj} [at]


A novel method for human activity recognition is presented. Given a video sequence containing human activity, the motion parameters of each frame are first computed using different motion parameter models. The likelihood of these observed motion parameters is optimally approximated, based directly on a multivariate Gaussian probabilistic model. The dynamic change of motion parameter likelihood in a video sequence is characterized using a continuous density hidden Markov model. Activity recognition is then posed as a motion parameter maximum likelihood estimation problem. Experimental results show that the method proposed here works well in recognizing such complex human activities as sitting, getting up from a chair, and some martial art actions.
[PDF] [BibTex]
X. Sun, C.-W. Chen, and B. S. Manjunath,
IEEE International Conference on Pattern Recognition (ICPR) 2002, vol. 1, pp. 443-446, Québec City, Canada, Aug. 2002.
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