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Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

[+] Author Affiliations
Wei Zhang

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai 200240, China

Xiangzhong Fang

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai 200240, China

Xiaokang Yang

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai 200240, China

Q. M. Jonathan Wu

University of Windsor, Department of Electrical and Computer Engineering, 401 Sunset, Windsor, Ontario, Canada N9B 3P4

J. Electron. Imaging. 16(2), 023013 (May 04, 2007). doi:10.1117/1.2731329
History: Received March 16, 2006; Revised February 06, 2007; Accepted February 19, 2007; Published May 04, 2007
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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.

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© 2007 SPIE and IS&T

Topics

Cameras ; Modeling

Citation

Wei Zhang ; Xiangzhong Fang ; Xiaokang Yang and Q. M. Jonathan Wu
"Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes", J. Electron. Imaging. 16(2), 023013 (May 04, 2007). ; http://dx.doi.org/10.1117/1.2731329


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