3D human pose recovery from image by efficient visual feature selection

Chen, Cheng, Yang, Yi, Nie, Feiping and Odobez, Jean-Marc (2011) 3D human pose recovery from image by efficient visual feature selection. Computer Vision and Image Understanding, 115 3: 290-299. doi:10.1016/j.cviu.2010.11.007

Author Chen, Cheng
Yang, Yi
Nie, Feiping
Odobez, Jean-Marc
Title 3D human pose recovery from image by efficient visual feature selection
Journal name Computer Vision and Image Understanding   Check publisher's open access policy
ISSN 1077-3142
Publication date 2011-03-01
Sub-type Article (original research)
DOI 10.1016/j.cviu.2010.11.007
Open Access Status Not yet assessed
Volume 115
Issue 3
Start page 290
End page 299
Total pages 10
Place of publication Maryland Heights, MO, United States
Publisher Academic Press
Language eng
Subject 1712 Software
1711 Signal Processing
1707 Computer Vision and Pattern Recognition
Abstract In this paper we propose a new examplar-based approach to recover 3D human poses from monocular images. Given the visual feature of each frame, pose retrieval is first conducted in the examplar database to find relevant pose candidates. Then, dynamic programming is applied on the pose candidates to recover a continuous pose sequence. We make two contributions within this framework. First, we propose to use an efficient feature selection algorithm to select effective visual feature components. The task is formulated as a trace-ratio criterion which measures the score of the selected feature component subset, and the criterion is efficiently optimized to achieve the global optimum. The selected components are used instead of the original full feature set to improve the accuracy and efficiency of pose recovery. As second contribution, we propose to use sparse representation to retrieve the pose candidates, where the measured visual feature is expressed as a sparse linear combination of the examplars in the database. Sparse representation ensures that semantically similar poses have larger probability to be retrieved. The effectiveness of our approach is validated quantitatively through extensive evaluations on both synthetic and real data, and qualitatively by inspecting the results of the real time system we have implemented.
Keyword Feature selection
Motion understanding
Pose recovery
Sparse representation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Special issue on feature-oriented Image and video computing for extracting contexts and semantics

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 27 times in Thomson Reuters Web of Science Article | Citations
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