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A computationally efficient importance sampling tracking algorithm

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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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Authors and Affiliations

  1. Computer and Software Engineering Department, École Polytechnique de Montréal, Montreal, QC , H3C 3A7, Canada

    Rana Farah, Qifeng Gan, J. M. Pierre Langlois & Guillaume-Alexandre Bilodeau

  2. Electrical Engineering Department, École Polytechnique de Montréal, Montreal, QC , H3C 3A7, Canada

    Yvon Savaria

Authors
  1. Rana Farah
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  2. Qifeng Gan
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  3. J. M. Pierre Langlois
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  4. Guillaume-Alexandre Bilodeau
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  5. Yvon Savaria
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Correspondence to Rana Farah.

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Farah, R., Gan, Q., Langlois, J.M.P. et al. A computationally efficient importance sampling tracking algorithm. Machine Vision and Applications 25, 1761–1777 (2014). https://doi.org/10.1007/s00138-014-0630-5

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  • DOI: https://doi.org/10.1007/s00138-014-0630-5

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