An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

• Published in: Computer modeling in engineering & sciences 2020-01, Vol.124 (1), p.129-149
• Main Authors: Wan, Qin, Zhu, Xiaolin, Xiao, Yueping, Yan, Jine, Chen, Guoquan, Sun, Mingui
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Constraint modelling; Fields (mathematics); Markov Randomfi Eld; Moving object recognition; Non-Parametric Method; Nonparametric statistics; Object Detection; Subtraction; Surveillance systems
• ISSN: 1526-1492; 1526-1506; 1526-1506
• DOI: 10.32604/cmes.2020.09397

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 non-parametric 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.