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

Authors:
Tomás Crivelli;Bruno Cernuschi-Frias;Patrick Bouthemy;Jian-Feng Yao
Affiliations:
LIPSIRN, University of Buenos Aires, Paseo Colón 850, 1063 Bs. As., Argentina;LIPSIRN, University of Buenos Aires, Paseo Colón 850, 1063 Bs. As., Argentina and CONICET, Argentina;INRIA Rennes, Campus de Beaulieu, 35042 Rennes Cedex, France;IRMAR, University of Rennes I, Campus de Beaulieu, 35042 Rennes Cedex, France
Venue:
Pattern Recognition Letters
Year:
2010

Citing 15
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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.