In order to reconstruct 3D human pose from multi-view images accurately and effectively

a novel human pose estimation algorithm based on multi-kernel sparse coding is proposed.First

for the ambiguity of human pose estimation between the consecutive frames

we describe multi-view images using a special HA-SIFT descriptor

in which the human body local topology

relative coordinates and appearance information are encoded simultaneously;then

an objective function is established within the framework of multi-kernel learning

it takes both intrinsic manifold structure of the feature space and geometrical information of the pose space into consideration.The sparse coding

over-complete dictionary and multi-kernel weight are updated by optimizing the objective function iteratively in the Hilbert space;finally

the corresponding 3D human pose of the unknown input image is estimated by a linear combination of the bases of the human pose dictionary.The experimental results show that our proposed method provides higher accuracy of human pose estimation compared with kernel sparse coding

Laplace sparse coding and Bayesian sparse coding.