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Journal of Electronic Imaging

Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes
Author(s): Wei Zhang; Xiangzhong Fang; Xiaokang Yang; Q. M. Jonathan Wu
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Paper Abstract

The Gaussian mixture model (GMM) is an important metric for moving objects segmentation and is fit to deal with the gradual changes of illumination and the repetitive motions of scene elements. However, the performance of the GMM may be plagued by the complex motion of the dynamic background such as waving trees and flags fluttering. A spatiotemporal Gaussian mixture model (STGMM) is proposed to handle the complex motion of the background by considering every background pixel to be fluctuating both in intensity and in its neighboring region. A new matching rule is defined to incorporate the spatial information. Experimental results on typical scenes show that STGMM can segment the moving objects correctly in complex scenes. Quantitative evaluations demonstrate that the proposed STGMM performs better than GMM.

Paper Details

Date Published: 1 April 2007
PDF: 6 pages
J. Electron. Imag. 16(2) 023013 doi: 10.1117/1.2731329
Published in: Journal of Electronic Imaging Volume 16, Issue 2
Show Author Affiliations
Wei Zhang, Shanghai Jiao Tong Univ. (China)
Xiangzhong Fang, Shanghai Jiao Tong Univ. (China)
Xiaokang Yang, Shanghai Jiao Tong Univ. (China)
Q. M. Jonathan Wu, National Research Council Canada (Canada)

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