@Article{cmes.2020.09397,
AUTHOR = {Qin Wan, Xiaolin Zhu, Yueping Xiao, Jine Yan, Guoquan Chen, Mingui Sun},
TITLE = {An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {1},
PAGES = {129--149},
URL = {http://www.techscience.com/CMES/v124n1/39385},
ISSN = {1526-1506},
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 the
fused features combine the colours, gradients and positions. 2) A Markov random
field method based on this adaptive background model via the constraint of the
spatial context is proposed to extract objects. 3) The posterior function is maximized efficiently by using an improved ant colony system algorithm. Extensive
experiments show that the proposed method demonstrates a better performance
than many existing state-of-the-art methods.},
DOI = {10.32604/cmes.2020.09397}
}