10.5281/zenodo.4707135
https://zenodo.org/records/4707135
oai:zenodo.org:4707135
Konstantinos Gkountakos
Konstantinos Gkountakos
0000-0001-7711-3773
Information Technologies Institute CERTH
Konstantinos Ioannidis
Konstantinos Ioannidis
Information Technologies Institute CERTH
Theodora Tsikrika
Theodora Tsikrika
Information Technologies Institute CERTH
Stefanos Vrochidis
Stefanos Vrochidis
Information Technologies Institute CERTH
Ioannis Kompatsiaris
Ioannis Kompatsiaris
Information Technologies Institute CERTH
Crowd Violence Detection from Video Footage
Zenodo
2021
Crowd analysis
Violence detection
(near) real-time
3D-CNN
2021-04-21
eng
10.5281/zenodo.4707134
https://zenodo.org/communities/connexions-h2020
https://zenodo.org/communities/m4d
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
833115
Prediction and Visual Intelligence for Security Information
European Commission
10.13039/501100000780
786731
InterCONnected NEXt-Generation Immersive IoT Platform of Crime and Terrorism DetectiON, PredictiON, InvestigatiON, and PreventiON Services