Human motion estimation from a single view

Author(s):  
Yi Sun ◽  
Hongjie An ◽  
Chengyi Cui
Author(s):  
B. Rosenhahn ◽  
U. G. Kersting ◽  
A. W. Smith ◽  
J. K. Gurney ◽  
T. Brox ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Wanyi Li ◽  
Jifeng Sun

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.


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