TY - GEN
T1 - Discriminative estimation of 3D human pose using Gaussian processes
AU - Zhao, Xu
AU - Ning, Huazhong
AU - Liu, Yuncai
AU - Huang, Thomas
PY - 2008
Y1 - 2008
N2 - In this paper, we present an efficient discriminative method for human pose estimation. This method learns a direct mapping from visual observations to human body configurations. The framework requires that the visual features should be powerful enough to discriminate the subtle differences between similar human poses. We propose to describe the image features using salient interest points that are represented by SIFT-like descriptors. The descriptor encode the position, appearance, and local structural information simultaneously. Bag-of-words representation is used to model the distribution of feature space. The descriptor can tolerate a range of illumination and position variations because it is computed on overlapped patches. We use Gaussian process regression to model the mapping from visual observations to human poses. This probabilistic regression algorithm is effective and robust to the pose estimation problem. We test our approach on the HumanEva data set. Experimental results demonstrate that our approach achieves the state of the art performance.
AB - In this paper, we present an efficient discriminative method for human pose estimation. This method learns a direct mapping from visual observations to human body configurations. The framework requires that the visual features should be powerful enough to discriminate the subtle differences between similar human poses. We propose to describe the image features using salient interest points that are represented by SIFT-like descriptors. The descriptor encode the position, appearance, and local structural information simultaneously. Bag-of-words representation is used to model the distribution of feature space. The descriptor can tolerate a range of illumination and position variations because it is computed on overlapped patches. We use Gaussian process regression to model the mapping from visual observations to human poses. This probabilistic regression algorithm is effective and robust to the pose estimation problem. We test our approach on the HumanEva data set. Experimental results demonstrate that our approach achieves the state of the art performance.
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M3 - Conference contribution
AN - SCOPUS:77954855089
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
T2 - 2008 19th International Conference on Pattern Recognition, ICPR 2008
Y2 - 8 December 2008 through 11 December 2008
ER -