Mixed-state causal modeling for statistical KL-based motion texture tracking
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 (...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2010 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/19432 |
| Acceso en línea: | http://hdl.handle.net/11336/19432 |
| Access Level: | acceso abierto |
| Palabra clave: | Mixed-State Markov Models Motion Textures Visual Tracking Kullback-Leibler Divergence https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| Sumario: | 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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