1 April 2007 Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes
Author Affiliations +
J. of Electronic Imaging, 16(2), 023013 (2007). doi:10.1117/1.2731329
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.
Wei Zhang, Xiangzhong Fang, Xiaokang Yang, Q. M. Jonathan Wu, "Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes," Journal of Electronic Imaging 16(2), 023013 (1 April 2007). http://dx.doi.org/10.1117/1.2731329

Image segmentation

Motion models


Data modeling

Expectation maximization algorithms

Performance modeling

Control systems

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