1.School of Life Sciences and Technology,Xidian University,Xian 710071 2.College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003
Abstract Human motion analysis is one of the most active subjects in computer vision. Two improved dynamic texture models are proposed for human motion sequence description, binary dynamic texture model and tensor subspace dynamic texture model. A binary image is supposed to submit to Bernoulli distribution, and the logistic principle component analysis is used to learn the parameters of the binary dynamic texture model. In tensor subspace dynamic texture model, a binary image is treated as a tensor with dimensions of column and row reduced by tensor subspace analysis, and then it is transformed to a low-dimensional gray image. The dynamic texture model is applied to describe the gray image sequence. Experimental results on human activity and gait databases show the validity of the two proposed improved dynamic texture models.
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2010, Vol. 23 
