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	Dynamic background modeling using tensor representation and ant colony optimization
	Peng LiZhong ; Zhang Fan ; Zhou BingYin
	2017
关键词	background modeling dynamic scenes tensor representation ant colony optimization OBJECT DETECTION DENSITY-ESTIMATION SUBTRACTION SURVEILLANCE EIGENVALUES SEGMENTATION ALGORITHMS VIDEOS SCENES
英文摘要	Background modeling and subtraction is a fundamental problem in video analysis. Many algorithms have been developed to date, but there are still some challenges in complex environments, especially dynamic scenes in which backgrounds are themselves moving, such as rippling water and swaying trees. In this paper, a novel background modeling method is proposed for dynamic scenes by combining both tensor representation and swarm intelligence. We maintain several video patches, which are naturally represented as higher order tensors, to represent the patterns of background, and utilize tensor low-rank approximation to capture the dynamic nature. Furthermore, we introduce an ant colony algorithm to improve the performance. Experimental results show that the proposed method is robust and adaptive in dynamic environments, and moving objects can be perfectly separated from the complex dynamic background.; National Natural Science Foundation of China [11301137, 11371036]; National Science Foundation of Hebei Province of China [A2014205100]; SCI(E); ARTICLE; 11; 2287-2302; 60
语种	英语
出处	知网 ; SCI
出版者	SCIENCE CHINA-MATHEMATICS
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/479500]
专题	数学科学学院
推荐引用方式 GB/T 7714	Peng LiZhong,Zhang Fan,Zhou BingYin. Dynamic background modeling using tensor representation and ant colony optimization. 2017-01-01.

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