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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 671419, 21 pages
Research Article

Human Motion Estimation Based on Low Dimensional Space Incremental Learning

School of Electronic and Information Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China

Received 28 September 2014; Revised 5 January 2015; Accepted 9 January 2015

Academic Editor: Mohamed Djemai

Copyright © 2015 Wanyi Li and Jifeng Sun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a novel algorithm called low dimensional space incremental learning (LDSIL) to estimate the human motion in 3D from the silhouettes of human motion multiview images. The proposed algorithm takes the advantage of stochastic extremum memory adaptive searching (SEMAS) and incremental probabilistic dimension reduction model (IPDRM) to collect new high dimensional data samples. The high dimensional data samples can be selected to update the mapping from low dimensional space to high dimensional space, so that incremental learning can be achieved to estimate human motion from small amount of samples. Compared with three traditional algorithms, the proposed algorithm can make human motion estimation achieve a good performance in disambiguating silhouettes, overcoming the transient occlusion, and reducing estimation error.