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PCM
2004
Springer

Approximating Inference on Complex Motion Models Using Multi-model Particle Filter

10 years 1 months ago
Approximating Inference on Complex Motion Models Using Multi-model Particle Filter
Abstract. Due to its great ability of conquering clutters, which is especially useful for high-dimensional tracking problems, particle filter becomes popular in the visual tracking community. One remained difficulty of applying the particle filter to high-dimensional tracking problems is how to propagate particles efficiently considering complex motions of the target. In this paper, we propose the idea of approximating the complex motion model using a set of simple motion models to deal with the tracking problems cumbered by complex motions. Then, we provide a practical way to do inference on the set of simple motion models instead of original complex motion model in the particle filter. This new variation of particle filter is termed as Multi-Model Particle Filter (MMPF). We apply our proposed MMPF to the problem of head motion tracking. Note that the defined head motions include both rigid motions and non-rigid motions. Experiments show that, when compared with the standard part...
Jianyu Wang, Debin Zhao, Shiguang Shan, Wen Gao
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PCM
Authors Jianyu Wang, Debin Zhao, Shiguang Shan, Wen Gao
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