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Violent Interaction Detection in Video Based on Deep Learning
Zhou PP(周培培); Ding QH(丁庆海); Luo HB(罗海波); Hou XL(侯幸林)
作者部门光电信息技术研究室
会议名称6th Conference on Advances in Optoelectronics and Micro/Nano-Optics, AOM 2017
会议日期April 23-26, 2017
会议地点Nanjing, China
会议主办者Jiangsu Optical Society; Southeast University; The Optical Society of America
会议录名称6th Conference on Advances in Optoelectronics and Micro/Nano-Optics
出版者IOP
出版地Bristol, UK
2017
页码1-9
收录类别EI ; CPCI(ISTP)
EI收录号20172803930012
WOS记录号WOS:000412799900044
产权排序1
ISSN号1742-6588
摘要Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/20795
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, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
3.Key Laboratory of Opto-Electronic Information Processing CAS, Shenyang, 110016, China
4.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang, 110016, China
5.Space Star Technology Co. LTD, Beijing, 100086, China
推荐引用方式
GB/T 7714
Zhou PP,Ding QH,Luo HB,et al. Violent Interaction Detection in Video Based on Deep Learning[C]//Jiangsu Optical Society; Southeast University; The Optical Society of America. Bristol, UK:IOP,2017:1-9.
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