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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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