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Violence detection in surveillance video using low-level features
Zhou PP(周培培)1,2,3,4; Ding QH(丁庆海)1,5; Luo HB(罗海波)1,3,4; Hou XL(侯幸林)1,2,3,4
Department光电信息技术研究室
Source PublicationPLOS ONE
ISSN1932-6203
2018
Volume13Issue:10Pages:1-15
Indexed BySCI
WOS IDWOS:000446342400026
Contribution Rank1
AbstractIt 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.
Language英语
WOS SubjectMultidisciplinary Sciences
WOS Research AreaScience & Technology - Other Topics
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23414
Collection光电信息技术研究室
Corresponding AuthorZhou PP(周培培)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning Province, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang, Liaoning Province, China
4.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, China
5.Space Star Technology Company Limited, Beijing, China
Recommended Citation
GB/T 7714
Zhou PP,Ding QH,Luo HB,et al. Violence detection in surveillance video using low-level features[J]. PLOS ONE,2018,13(10):1-15.
APA Zhou PP,Ding QH,Luo HB,&Hou XL.(2018).Violence detection in surveillance video using low-level features.PLOS ONE,13(10),1-15.
MLA Zhou PP,et al."Violence detection in surveillance video using low-level features".PLOS ONE 13.10(2018):1-15.
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