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

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.