Surveillance video synopsis in the compressed domain for fast video browsing

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Surveillance video synopsis in the compressed domain for fast video browsing
	Wang, Shi-zheng 1,2; Wang, Zhong-yuan 1; Hu, Rui-min 1
发表期刊	JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
	2013-11-01
卷号	24 期号:8 页码:1431-1442
文章类型	Article
摘要	The traditional pixel-domain based video analysis methods have taken dominated places for long. However, due to the rapidly increasing volume and resolution of surveillance video, the desirable fast and scalable browsing encounters significant challenges in terms of efficiency and flexibility. Under this circumstance, operating surveillance video in compressed domain has aroused great concern in academy and industry. In order to perform the intelligent video analysis task on the premise of preserving accuracy and controlling complexity, this paper presents a compressed-domain approach for massive surveillance video synopsis generation, labeling and browsing. The main work and achievements include: (1) a compressed-domain scheme is established to condense the compressed surveillance video and record the synopsis results; (2) a background modeling method via the Motion Vector based Local Binary Pattern (MVLBP) is introduced to extract moving objects in an efficient way; (3) an object flags based synopsis labeling method is proposed to represent the object regions as well as their display modes in a flexible way. Experimental results show that the video analysis system based on this framework can provide not only efficient synopsis generation but also flexible scalable or playback browsing. (C) 2013 Elsevier Inc. All rights reserved.
关键词	Surveillance Video Compressed Domain Video Synopsis Video Labeling Scalable Browsing Fast Browsing Background Modeling Intelligent Video
WOS标题词	Science & Technology ; Technology
关键词[WOS]	MOVING OBJECT SEGMENTATION ; VISUAL SURVEILLANCE ; CLASSIFICATION ; FRAMES ; MODEL
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000328590700017
引用统计	被引频次：16[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/173211/8938
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
作者单位	1.Wuhan Univ, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, 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, Shi-zheng,Wang, Zhong-yuan,Hu, Rui-min. Surveillance video synopsis in the compressed domain for fast video browsing[J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,2013,24(8):1431-1442.
APA	Wang, Shi-zheng,Wang, Zhong-yuan,&Hu, Rui-min.(2013).Surveillance video synopsis in the compressed domain for fast video browsing.JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,24(8),1431-1442.
MLA	Wang, Shi-zheng,et al."Surveillance video synopsis in the compressed domain for fast video browsing".JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 24.8(2013):1431-1442.