Title :
Probabilistic motion parameter models for human activity recognition
Author :
Sun, Xinding ; Chen, Ching-Wei ; Manjunath, B.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
Abstract :
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.
Keywords :
Gaussian distribution; hidden Markov models; image recognition; image sequences; maximum likelihood estimation; motion estimation; video signal processing; continuous density hidden Markov model; human activity recognition; martial art actions; motion parameter likelihood; motion parameter maximum likelihood estimation problem; multivariate Gaussian probabilistic model; optimal approximation; probabilistic motion parameter models; sitting; video sequence; Art; Face recognition; Hidden Markov models; Humans; Image motion analysis; Image recognition; Maximum likelihood estimation; Motion detection; Optical sensors; Video sequences;
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
Print_ISBN :
0-7695-1695-X
DOI :
10.1109/ICPR.2002.1044751