This paper introduces the Path Relinking Particle Filter (PRPF) algorithm for improving estimation problems in human motion capture. PRPF hybridizes both Particle Filter and Path Relinking frameworks. The proposed algorithm increases the performance of general Particle Filter by improving the quality of the estimate, by adapting computational load to problem constraints and by reducing the number of required evaluations of the weighting function. We have applied the PRPF algorithm to 2D human pose estimation. Experimental results show that PRPF drastically reduces the MSE value to obtain the set of markers with respect to Condensation and Sampling Importance Resampling (SIR) algorithms. © Springer-Verlag 2004.
CITATION STYLE
Pantrigo, J. J., Sánchez, Á., Gianikellis, K., & Duarte, A. (2004). Path relinking particle filter for human body pose estimation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3138, 653–661. https://doi.org/10.1007/978-3-540-27868-9_71
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