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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
作者部门光电信息技术研究室
发表期刊PLOS ONE
ISSN1932-6203
2018
卷号13期号:10页码:1-15
收录类别SCI
WOS记录号WOS:000446342400026
产权排序1
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.
语种英语
WOS类目Multidisciplinary Sciences
WOS研究方向Science & Technology - Other Topics
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/23414
专题光电信息技术研究室
通讯作者Zhou PP(周培培)
作者单位1.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
推荐引用方式
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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