Conference paper Open Access
Crowd Violence Detection from Video Footage
Konstantinos Gkountakos;
Konstantinos Ioannidis;
Theodora Tsikrika;
Stefanos Vrochidis;
Ioannis Kompatsiaris
Surveillance systems currently deploy a variety of devices that can capture visual content (such as CCTV, body-worn cameras, and smartphone cameras), thus rendering the monitoring of the video footage obtained from multiple such devices a complex task. This becomes especially challenging when monitoring social events that involve large crowds, particularly when there is a risk of crowd violence. This paper presents and demonstrates a crowd violence detection system that can process, analyze, and alert the potential stakeholders when violence-related content is identified in crowd-based video footage. Based on deep neural networks, the proposed end-to-end framework utilizes a 3D Convolutional Neural Network (CNN) to deal with the (near) real-time analysis of video streams and video files for crowd violence detection. The framework is trained, evaluated, and demonstrated using the most recent dataset related to crowd-violence, namely the Violent Flows dataset. The presented framework is provided as a standalone application for desktop environments and can analyze video streams and video files.
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- Publication date:
- April 21, 2021
- DOI:
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- Keyword(s):
- Crowd analysis Violence detection (near) real-time 3D-CNN
- Grants:
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European Commission:
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PREVISION - Prediction and Visual Intelligence for Security Information (833115)
- CONNEXIONs - InterCONnected NEXt-Generation Immersive IoT Platform of Crime and Terrorism DetectiON, PredictiON, InvestigatiON, and PreventiON Services (786731)
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PREVISION - Prediction and Visual Intelligence for Security Information (833115)
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International