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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 7/2014

01.10.2014 | Original Paper

A computationally efficient importance sampling tracking algorithm

verfasst von: Rana Farah, Qifeng Gan, J. M. Pierre Langlois, Guillaume-Alexandre Bilodeau, Yvon Savaria

Erschienen in: Machine Vision and Applications | Ausgabe 7/2014

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Abstract

This paper proposes a computationally efficient importance sampling algorithm applicable to computer vision tracking. The algorithm is based on the CONDENSATION algorithm, but it avoids expensive operations that are costly in real-time embedded systems. It also includes a method that reduces the number of particles during execution and a new resampling scheme. Our experiments demonstrate that the proposed algorithm is as accurate as the CONDENSATION algorithm. Depending on the processed sequence, the acceleration with respect to CONDENSATION can reach 7\(\times \) for 50 particles, 12\(\times \) for 100 particles and 58\(\times \) for 200 particles.
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Metadaten
Titel
A computationally efficient importance sampling tracking algorithm
verfasst von
Rana Farah
Qifeng Gan
J. M. Pierre Langlois
Guillaume-Alexandre Bilodeau
Yvon Savaria
Publikationsdatum
01.10.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 7/2014
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-014-0630-5

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