Violent Interaction Detection in Video Based on Deep Learning
Open Access
- 7 June 2017
- journal article
- research article
- Published by IOP Publishing in Journal of Physics: Conference Series
- Vol. 844 (1), 012044
- https://doi.org/10.1088/1742-6596/844/1/012044
Abstract
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.This publication has 12 references indexed in Scilit:
- Automatic Fight Detection Based on Motion AnalysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- A new method for violence detection in surveillance scenesMultimedia Tools and Applications, 2015
- Fast Fight DetectionPLOS ONE, 2015
- Joint Audio-Visual Words for Violent Scenes Detection in MoviesPublished by Association for Computing Machinery (ACM) ,2014
- Dense Trajectories and Motion Boundary Descriptors for Action RecognitionInternational Journal of Computer Vision, 2013
- 3D Convolutional Neural Networks for Human Action RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Violence Detection in Video Using Computer Vision TechniquesLecture Notes in Computer Science, 2011
- A Duality Based Approach for Realtime TV-L 1 Optical FlowPublished by Springer Science and Business Media LLC ,2007
- On Space-Time Interest PointsInternational Journal of Computer Vision, 2005
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998