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Institute for Research in Computer Science and Random Systems |  |
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Institute for Research in Computer Science and Random Systems | |
| HAL: hal-00171409, version 1 |
| DOI: 10.1007/s11263-006-0003-2 |
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| International Journal of Computer Vision 74, 1 (2007) 75-102 |
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| Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods |
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| Elise Arnaud 1, 2Étienne Mémin 3 |
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| (2007-08) |
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| The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the state-space exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging real-world image sequences. |
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| 1: | Laboratoire Jean Kuntzmann (LJK) |
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CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre-Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology |
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| 2: | PERCEPTION (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann) |
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INRIA – Laboratoire Jean Kuntzmann – CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG) – Université Pierre-Mendès-France - Grenoble II |
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| 3: | VISTA (INRIA - IRISA) |
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CNRS : UMR6074 – INRIA – Université de Rennes 1 – Institut National des Sciences Appliquées (INSA) |
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| sequential Monte Carlo methods – optimal importance function – Rao-Blackwellization – validation gate – point tracking – planar structure tracking |
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| hal-00171409, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00171409 | |
| oai:hal.archives-ouvertes.fr:hal-00171409 | |
| From: Brigitte Bidegaray-Fesquet | |
| Submitted on: Wednesday, 12 September 2007 13:15:00 | |
| Updated on: Friday, 25 March 2011 13:39:36 | |
