Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.765767
Title: An efficient stochastic framework for 3D human motion tracking
Authors: Ni, B.
Winkler, S. 
Kassim, A.A. 
Keywords: Articulated 3D human motion tracking
IMM Kalman particle filter
Simulated physical force/moment
Issue Date: 2008
Source: Ni, B., Winkler, S., Kassim, A.A. (2008). An efficient stochastic framework for 3D human motion tracking. Proceedings of SPIE - The International Society for Optical Engineering 6805 : -. ScholarBank@NUS Repository. https://doi.org/10.1117/12.765767
Abstract: In this paper, we present a stochastic framework for articulated 3D human motion tracking. Tracking full body human motion is a challenging task, because the tracking performance normally suffers from several issues such as self-occlusion, foreground segmentation noise and high computational cost. In our work, we use explicit 3D reconstructions of the human body based on a visual hull algorithm as our system input, which effectively eliminates self-occlusion. To improve tracking efficiency as well as robustness, we use a Kalman particle filter framework based on an interacting multiple model (IMM). The posterior density is approximated by a set of weighted particles, which include both sample means and covariances. Therefore, tracking is equivalent to searching the maximum a posteriori (MAP) of the probability distribution. During Kalman filtering, several dynamical models of human motion (e.g., zero order, first order) are assumed which interact with each other for more robust tracking results. Our measurement step is performed by a local optimization method using simulated physical force/moment for 3D registration. The likelihood function is designed to be the fitting score between the reconstructed human body and our 3D human model, which is composed of a set of cylinders. This proposed tracking framework is tested on a real motion sequence. Our experimental results show that the proposed method improves the sampling efficiency compared with most particle filter based methods and achieves high tracking accuracy. © 2008 SPIE-IS&T.
Source Title: Proceedings of SPIE - The International Society for Optical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/69306
ISBN: 9780819469779
ISSN: 0277786X
DOI: 10.1117/12.765767
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