Violence detection in surveillance video using low-level features

doi:10.1371/journal.pone.0203668

CORC > 金属研究所 > 中国科学院金属研究所

	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
刊名	PLOS ONE
	2018-10-03
卷号	13 期号:10 页码:15
ISSN号	1932-6203
DOI	10.1371/journal.pone.0203668
通讯作者	Zhou, Peipei(zhoupeipei@sia.cn)
英文摘要	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.
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000446342400026
内容类型	期刊论文
源URL	[http://ir.imr.ac.cn/handle/321006/129855]
专题	金属研究所_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.