CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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International Journal of Computer Vision
Discriminative Density Propagation for 3D Human Motion Estimation
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International Journal of Computer Vision
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
3D human motion tracking based on a progressive particle filter
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3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Stochastic search methods, such as the annealed particle filter (APF) and its variants, are used widely in human pose tracking due to their reliability. In this paper, we propose a method that improves stochastic search by using two novel steps: first, by reusing samples across annealing layers, and second, by fitting an adaptive parametric density to the samples for diffusion. We compare our proposed method, called parametric annealing (PA), to APF as well as to the recently published interacting simulating annealing (ISA) on the Human Eva I dataset. The results show that PA tracks more accurately than APF despite using less than 50% of the samples, and also tracks more accurately than an ISA configuration that uses the same number of samples. Furthermore, we describe a framework to select the optimum parameters for APF, ISA, and PA that takes into account their stochastic nature. Using our framework, the computational overhead for tracking may be reduced by up to 40% with no loss of performance. Finally, we compare our method to discriminative methods.