Violence detection in surveillance video using low-level features
文献类型：期刊论文
| 作者 | Zhou PP(周培培)1,3,4,5 ; Ding QH(丁庆海)1,2; Luo HB(罗海波)1,4,5; Hou XL(侯幸林)1,3,4,5
|
| 刊名 | PLOS ONE
 |
| 出版日期 | 2018 |
| 卷号 | 13期号:10页码:1-15 |
| ISSN号 | 1932-6203 |
| 产权排序 | 1 |
| 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 |
| WOS记录号 | WOS:000446342400026 |
| 源URL | [http://ir.sia.cn/handle/173321/23414]  |
| 专题 | 沈阳自动化研究所_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.Space Star Technology Company Limited, Beijing, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang, Liaoning Province, China 5.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。