Qin Wan1,2,*, Xiaolin Zhu1, Yueping Xiao1, Jine Yan1, Guoquan Chen1, Mingui Sun3
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 129-149, 2020, DOI:10.32604/cmes.2020.09397
Abstract Detecting moving objects in the stationary background is an important
problem in visual surveillance systems. However, the traditional background subtraction method fails when the background is not completely stationary and
involves certain dynamic changes. In this paper, according to the basic steps of
the background subtraction method, a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the
Markov random field. Concretely, the contributions are as follows: 1) A new nonparametric strategy is utilized to model the background, based on an improved
kernel density estimation; this approach uses an adaptive bandwidth, and… More >
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