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ICIAR
2009
Springer

3D Human Pose Estimation from Static Images Using Local Features and Discriminative Learning

8 years 9 months ago
3D Human Pose Estimation from Static Images Using Local Features and Discriminative Learning
In this paper an approach to recover the 3D human body pose from static images is proposed. We adopt a discriminative learning technique to directly infer the 3D pose from appearance-based local image features. We use simplified Gradient Location and Orientation histogram (GLOH) as our image feature representation. We then employ the gradient tree-boost regression to train a discriminative model for mapping from the feature space to the 3D pose space. The training and evaluation of our algorithm were conducted on the walking sequences of a synchronized video and 3D motion dataset. We show that appearancebased local features can be used for pose estimation even in cluttered environments. At the same time, the discriminatively learned model allows the 3D pose to be estimated in real time.
Suman Sedai, Farid Flitti, Mohammed Bennamoun, Du
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICIAR
Authors Suman Sedai, Farid Flitti, Mohammed Bennamoun, Du Q. Huynh
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