Violence detection in surveillance video using low-level features

doi:10.1371/journal.pone.0203668

IMR OpenIR

	Violence detection in surveillance video using low-level features
	Zhou, Peipei 1,2,3,4; Ding, Qinghai 1,5; Luo, Haibo 1,3,4; Hou, Xinglin 1,2,3,4
通讯作者	Zhou, Peipei(zhoupeipei@sia.cn)
	2018-10-03
发表期刊	PLOS ONE
ISSN	1932-6203
卷号	13 期号:10 页码:15
摘要	It is very important to automatically detect violent behaviors in video surveillance scenarios, for instance, railway stations, gymnasiums and psychiatric centers. However, the previous detection methods usually extract descriptors around the spatiotemporal interesting points or extract statistic features in the motion regions, leading to limited abilities to effectively detect video-based violence activities. To address this issue, we propose a novel method to detect violence sequences. Firstly, the motion regions are segmented according to the distribution of optical flow fields. Secondly, in the motion regions, we propose to extract two kinds of low-level features to represent the appearance and dynamics for violent behaviors. The proposed low-level features are the Local Histogram of Oriented Gradient (LHOG) descriptor extracted from RGB images and the Local Histogram of Optical Flow (LHOF) descriptor extracted from optical flow images. Thirdly, the extracted features are coded using Bag of Words (BoW) model to eliminate redundant information and a specific-length vector is obtained for each video clip. At last, the video-level vectors are classified by Support Vector Machine (SVM). Experimental results on three challenging benchmark datasets demonstrate that the proposed detection approach is superior to the previous methods.
DOI	10.1371/journal.pone.0203668
收录类别	SCI
语种	英语
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000446342400026
出版者	PUBLIC LIBRARY SCIENCE
引用统计	被引频次：55[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/129856
专题	中国科学院金属研究所
通讯作者	Zhou, Peipei
作者单位	1.Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Liaoning, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Key Lab Optoelect Informat Proc, Shenyang, Liaoning, Peoples R China 4.Key Lab Image Understanding & Comp Vis, Shenyang, Liaoning, Peoples R China 5.Space Star Technol Co Ltd, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Peipei,Ding, Qinghai,Luo, Haibo,et al. Violence detection in surveillance video using low-level features[J]. PLOS ONE,2018,13(10):15.
APA	Zhou, Peipei,Ding, Qinghai,Luo, Haibo,&Hou, Xinglin.(2018).Violence detection in surveillance video using low-level features.PLOS ONE,13(10),15.
MLA	Zhou, Peipei,et al."Violence detection in surveillance video using low-level features".PLOS ONE 13.10(2018):15.