Video-Based Human Motion Estimation by Part-Whole Gait Manifold Learning

2011 ◽  
pp. 215-261 ◽  
Author(s):  
Guoliang Fan ◽  
Xin Zhang
Keyword(s):  
Motion Estimation ◽  
Manifold Learning ◽  
Human Motion ◽  
Human Motion Estimation

A System for Marker-Less Human Motion Estimation

2005 ◽  
pp. 230-237 ◽  
Author(s):  
B. Rosenhahn ◽  
U. G. Kersting ◽  
A. W. Smith ◽  
J. K. Gurney ◽  
T. Brox ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Human Motion ◽  
Human Motion Estimation

Human motion estimation from a single view

2000 ◽  
Author(s):  
Yi Sun ◽  
Hongjie An ◽  
Chengyi Cui
Keyword(s):  
Motion Estimation ◽  
Human Motion ◽  
Single View ◽  
Human Motion Estimation

scholarly journals Human Motion Estimation Based on Low Dimensional Space Incremental Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-21
Author(s):  
Wanyi Li ◽  
Jifeng Sun
Keyword(s):  
Motion Estimation ◽  
Incremental Learning ◽  
Dimensional Space ◽  
Estimation Error ◽  
High Dimensional Data ◽  
Human Motion ◽  
High Dimensional ◽  
Multiview Images ◽  
Low Dimensional ◽  
Human Motion Estimation

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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