IAM   02674
INSTITUTO ARGENTINO DE MATEMATICA ALBERTO CALDERON
Unidad Ejecutora - UE
artículos
Título:
"Mixed-state causal modeling for statistical KL-based motion texture tracking".
Revista:
PATTERN RECOGNITION LETTERS
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2010 vol. 31 p. 2286 - 2286
ISSN:
0167-8655
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 KullbackĀ–Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.