He, L;
Wang, G;
Liao, Q;
Xue, J-H;
(2016)
Latent variable pictorial structure for human pose estimation on depth images.
Neurocomputing
, 203
pp. 52-61.
10.1016/j.neucom.2016.04.009.
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Abstract
Prior models of human pose play a key role in state-of-the-art techniques for monocular pose estimation. However, a simple Gaussian model cannot represent well the prior knowledge of the pose diversity on depth images. In this paper, we develop a latent variable-based prior model by introducing a latent variable into the general pictorial structure. Two key characteristics of our model (we call Latent Variable Pictorial Structure) are as follows: (1) it adaptively adopts prior pose models based on the estimated value of the latent variable; and (2) it enables the learning of a more accurate part classifier. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in recognition rate on the public datasets.
Type: | Article |
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Title: | Latent variable pictorial structure for human pose estimation on depth images |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.neucom.2016.04.009 |
Publisher version: | http://dx.doi.org/10.1016/j.neucom.2016.04.009 |
Language: | English |
Additional information: | Copyright © 2016. This manuscript version is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International licence (CC BY-NC-ND 4.0). This licence allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licences are available at http://creativecommons.org/licenses/by/4.0. Access may be initially restricted by the publisher. |
Keywords: | Pose estimation; Pictorial structure; Latent variable; Body silhouette; Regression forest; Depth images |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1492859 |
1. | United States | 22 |
2. | China | 6 |
3. | United Kingdom | 2 |
4. | Romania | 2 |
5. | Switzerland | 1 |
6. | Russian Federation | 1 |
7. | Singapore | 1 |
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