Human body pose estimation based on histograms of oriented gradients and Relevance Vector Machine

Author

Deng, Lin ; Jiang, Min ; Tang, J.

Author_Institution

Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

3365

Lastpage

3369

Abstract

In this paper, a new method for the estimation of 3D human body poses from monocular images is proposed. Histograms of oriented gradients are used as the features for modeling human body poses. Human body poses are represented as 3D limb angles, which can remove the structure information from pose vector. Relevance Vector Machine is used to infer the mapping from image features to body poses. Experiments show that the proposed method is robust to camera views and can lead more accurate results than other pose estimation methods.

Keywords

artificial limbs; feature extraction; gradient methods; image representation; medical image processing; pose estimation; 3D limb angles; feature extraction; human body pose estimation; image representation; monocular images; oriented gradient histograms; relevance vector machine; Cameras; Estimation; Joints; Shape; Support vector machines; Three dimensional displays; Vectors; 3D limb angles; Histograms of oriented gradients; Relevance Vector Machine; pose estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6084189

Filename

6084189