Artículo
Mixed-state causal modeling for statistical KL-based motion texture tracking
Fecha de publicación:
11/2010
Editorial:
Elsevier Science
Revista:
Pattern Recognition Letters
ISSN:
0167-8655
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
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Articulos(IAM)
Articulos de INST.ARG.DE MATEMATICAS "ALBERTO CALDERON"
Articulos de INST.ARG.DE MATEMATICAS "ALBERTO CALDERON"
Citación
Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng; Mixed-state causal modeling for statistical KL-based motion texture tracking; Elsevier Science; Pattern Recognition Letters; 31; 14; 11-2010; 2286-2294
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