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Silhouette-based human pose estimation using reversible jump Markov chain Monte Carlo

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3 Author(s)
Huang, S.-S. ; Dept. of Comput. Sci. & Inf. Eng., Nat. Univ., Taipei, Taiwan ; Fu, L.-C. ; Hsiao, P.-Y.

A novel approach for recovering the human body configuration based on the silhouette is presented. By considering pose inference as traversing the difference subspaces and using a data-driven mechanism, reversible jump Markov chain Monte Carlo (RJMCMC) can explore such solution space very efficiently. Experimental results are provided to demonstrate the efficiency and effectiveness of the proposed approach.

Published in:

Electronics Letters  (Volume:42 ,  Issue: 10 )