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A Generic Framework for 2D and 3D Upper Body Tracking

A Generic Framework for 2D and 3D Upper Body Tracking

Lei Zhang, Jixu Chen, Zhi Zeng, Qiang Ji
Copyright: © 2010 |Pages: 19
ISBN13: 9781605669007|ISBN10: 1605669008|EISBN13: 9781605669014
DOI: 10.4018/978-1-60566-900-7.ch007
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MLA

Zhang, Lei, et al. "A Generic Framework for 2D and 3D Upper Body Tracking." Machine Learning for Human Motion Analysis: Theory and Practice, edited by Liang Wang, et al., IGI Global, 2010, pp. 133-151. https://doi.org/10.4018/978-1-60566-900-7.ch007

APA

Zhang, L., Chen, J., Zeng, Z., & Ji, Q. (2010). A Generic Framework for 2D and 3D Upper Body Tracking. In L. Wang, L. Cheng, & G. Zhao (Eds.), Machine Learning for Human Motion Analysis: Theory and Practice (pp. 133-151). IGI Global. https://doi.org/10.4018/978-1-60566-900-7.ch007

Chicago

Zhang, Lei, et al. "A Generic Framework for 2D and 3D Upper Body Tracking." In Machine Learning for Human Motion Analysis: Theory and Practice, edited by Liang Wang, Li Cheng, and Guoying Zhao, 133-151. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-900-7.ch007

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Abstract

Upper body tracking is a problem to track the pose of human body from video sequences. It is difficult due to such problems as the high dimensionality of the state space, the self-occlusion, the appearance changes, etc. In this paper, we propose a generic framework that can be used for both 2D and 3D upper body tracking and can be easily parameterized without heavily depending on supervised training. We first construct a Bayesian Network (BN) to represent the human upper body structure and then incorporate into the BN various generic physical and anatomical constraints on the parts of the upper body. Unlike the existing upper body models, we aim at handling physically feasible body motions rather than only some typical motions. We also explicitly model the body part occlusion in the model, which allows to automatically detect the occurrence of self-occlusion and to minimize the effect of measurement errors on the tracking accuracy due to occlusion. Using the proposed model, upper body tracking can be performed through probabilistic inference over time. A series of experiments were performed on both monocular and stereo video sequences to demonstrate the effectiveness and capability of the model in improving upper body tracking accuracy and robustness.

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