Abstract
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.
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Pantrigo, J.J., Sánchez, Á., Gianikellis, K., Duarte, A. (2004). Path Relinking Particle Filter for Human Body Pose Estimation. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_71
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DOI: https://doi.org/10.1007/978-3-540-27868-9_71
Publisher Name: Springer, Berlin, Heidelberg
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