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Journal Articles Pattern Recognition Letters Year : 2010

Mixed-state causal modeling for statistical KL-based motion texture tracking

Abstract

We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback–Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.

Dates and versions

inria-00541270 , version 1 (30-11-2010)

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Cite

Tomas Crivelli, Bruno Cernuschi-Frias, Patrick Bouthemy, Jian-Feng Yao. Mixed-state causal modeling for statistical KL-based motion texture tracking. Pattern Recognition Letters, 2010, 31 (14), pp.2286-2294. ⟨10.1016/j.patrec.2010.06.016⟩. ⟨inria-00541270⟩
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