(MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance

doi:10.1109/ACCESS.2018.2812880

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	(MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance
	Wang, Kunfeng1,2 ; Gou, Chao1 ; Wang, Fei-Yue1,3
发表期刊	IEEE ACCESS
	2018
卷号	6 页码:15505-15520
文章类型	Article
摘要	In this paper, we propose a robust change detection method for intelligent visual surveillance. This method, named (MCD)-C-4, includes three major steps. First, a sample-based background model that integrates color and texture cues is built and updated over time. Second, multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation) are extracted by comparing the input frame with the background model, and a multi-view learning strategy is designed to online estimate the probability distributions for both foreground and background. The three features are approximately conditionally independent, making multi-view learning feasible. Pixel-wise foreground posteriors are then estimated with Bayes rule. Finally, the Markov random field (MRF) optimization and heuristic postprocessing techniques are used sequentially to improve accuracy. In particular, a two-layer MRF model is constructed to represent pixel-based and superpixel-based contextual constraints compactly. Experimental results on the CDnet dataset indicate that (MCD)-C-4 is robust under complex environments and ranks among the top methods.
关键词	Change Detection Multimodal Background Multi-view Learning Conditional Independence Markov Random Field
WOS标题词	Science & Technology ; Technology
DOI	10.1109/ACCESS.2018.2812880
关键词[WOS]	MOVING OBJECT DETECTION ; BACKGROUND SUBTRACTION ; SEGMENTATION ; TRACKING ; VIDEOS ; MODEL ; EDGE
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61533019 ; 71232006 ; 91720000)
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000429256000001
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/22003
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Innovat Ctr Parallel Vis, Qingdao 266000, Peoples R China 3.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Sys, Changsha 410073, Hunan, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Kunfeng,Gou, Chao,Wang, Fei-Yue. (MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance[J]. IEEE ACCESS,2018,6:15505-15520.
APA	Wang, Kunfeng,Gou, Chao,&Wang, Fei-Yue.(2018).(MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance.IEEE ACCESS,6,15505-15520.
MLA	Wang, Kunfeng,et al."(MCD)-C-4: A Robust Change Detection Method for Intelligent Visual Surveillance".IEEE ACCESS 6(2018):15505-15520.