M2SIR : A multi modal sequential importance resampling algorithm for particle filters

Abstract : We present a multi modal sequential importance resampling particle filter algorithm for object tracking. We consider a hidden state sequence linked to several observation sequences given by different sensors. In a particle filter based framework, each sensor provides a likelihood (weight) associated to each particle and simple rules are applied to merge the different weights such as addition or product. We propose an original algorithm based on likelihood ratios to merge the observations within the sampling step. The algorithm is compared with classic fusion operations on toy examples. Moreover, we show that the method gives satisfactory results on a real vehicle tracking application.
Type de document :
Communication dans un congrès
IEEE International Conference on Image Processing, Nov 2009, France. 4p, 2009
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https://hal.archives-ouvertes.fr/hal-00421055
Contributeur : Ifsttar Cadic <>
Soumis le : mercredi 30 septembre 2009 - 14:49:46
Dernière modification le : jeudi 13 septembre 2018 - 17:26:03

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  • HAL Id : hal-00421055, version 1

Citation

Yann Goyat, Thierry Chateau, Laurent Trassoudaine. M2SIR : A multi modal sequential importance resampling algorithm for particle filters. IEEE International Conference on Image Processing, Nov 2009, France. 4p, 2009. 〈hal-00421055〉

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