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.

Author affiliation: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina

Author affiliation: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina

Author affiliation: Bouthemy, Patrick. Irisa, Inria, Rennes, Francia;

Author affiliation: Yao, Jian-Feng. Institut National de Recherche en Informatique et en Automatique; Francia