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3D human posture estimation using the HOG features from monocular image

11 years 4 months ago
3D human posture estimation using the HOG features from monocular image
In this paper, we propose a method to estimate the 3D human posture from monocular image without using the markers. A 3D human body is expressed by a multi-joint model, and a set of the joint angles describes a posture. The proposed method estimates the posture using Histograms of Oriented Gradients(HOG) feature vectors that can express the shape of the object in the input image obtained from monocular camera. In addition, the feature dimension of the background region is reduced for reliability by principal component analysis (PCA) computed at every block of HOG. The joint angles in Human multi-joint model are estimated by linear regression analysis applied to its feature vector extracted from the input image. As a result of comparison experiment with the Shape Contexts features, the RMS error was reduced by about 5.35 degrees.
Katsunori Onishi, Tetsuya Takiguchi, Yasuo Ariki
Added 05 Nov 2009
Updated 05 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Katsunori Onishi, Tetsuya Takiguchi, Yasuo Ariki
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