Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSVT.2008.927108
Title: A hybrid framework for 3-D human motion tracking
Authors: Ni, B.
Kassim, A.A. 
Winkler, S.
Keywords: Articulated 3-D human motion tracking
Particle filter
Simulated physical force/moment
Vector quantization principal component analysis (VQPCA)
Issue Date: Aug-2008
Source: Ni, B., Kassim, A.A., Winkler, S. (2008-08). A hybrid framework for 3-D human motion tracking. IEEE Transactions on Circuits and Systems for Video Technology 18 (8) : 1075-1084. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSVT.2008.927108
Abstract: In this paper, we present a hybrid framework for articulated 3-D human motion tracking from multiple synchronized cameras with potential uses in surveillance systems. Although the recovery of 3-D motion provides richer information for event understanding, existing methods based on either deterministic search or stochastic sampling lack robustness or efficiency. We therefore propose a hybrid sample-and-refine framework that combines both stochastic sampling and deterministic optimization to achieve a good compromise between efficiency and robustness. Similar motion patterns are used to learn a compact low-dimensional representation of the motion statistics. Sampling in a low-dimensional space is implemented during tracking, which reduces the number of particles drastically. We also incorporate a local optimization method based on simulated physical force/moment into our framework, which further improves the optimality of the tracking. Experimental results on several real human motion sequences show the accuracy and robustness of our method, which also has a higher sampling efficiency than most particle filtering-based methods. © 2008 IEEE.
Source Title: IEEE Transactions on Circuits and Systems for Video Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/54267
ISSN: 10518215
DOI: 10.1109/TCSVT.2008.927108
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