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Indexed by:期刊论文

Date of Publication:2016-07-01

Journal:IEEE TRANSACTIONS ON MULTIMEDIA

Included Journals:SCIE、EI、Scopus

Volume:18

Issue:7

Page Number:1233-1244

ISSN No.:1520-9210

Key Words:Componentwise clustering feature point representation (CCFPR); pose estimation; sparse regression

Abstract:We propose two-dimensional pose estimation from a single range image of the human body, using sparse regression with a componentwise clustering feature point representation (CCFPR) model. CCFPR includes primary feature points and secondary feature points. The primary feature points consist of the torso center and five extremal points of human body, and further serve to classify all body pixels as the points of six body components. The secondary feature points are given by the cluster centers of each of the five components other than the torso, using K-means cluster. The human pose is obtained by learning a sparse projection matrix, which maps CCFPR to the skeleton points of human body, based on the assumption that each skeleton point be represented by a combination of a few feature points of associated body components. Experimental results on both virtual data and real data show that, under the sparse regression model with a suitably selected cluster number, CCFPR outperforms the random decision forest approach and prediction results of KINECT SENSOR V2.