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Using Gaussian Process Annealing Particle Filter for 3D Human Tracking

Abstract

We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loss.

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Correspondence to Leonid Raskin.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Raskin, L., Rivlin, E. & Rudzsky, M. Using Gaussian Process Annealing Particle Filter for 3D Human Tracking. EURASIP J. Adv. Signal Process. 2008, 592081 (2007). https://doi.org/10.1155/2008/592081

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Keywords

  • Gaussian Process
  • Process Annealing
  • Particle Filter
  • Motion Model
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