A Crowd Analysis Framework for Detecting Violence Scenes
Konstantinos Gkountakos, Konstantinos Ioannidis, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris
Abstract
This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all cases, while also performing the violence detection activities in (near) real-time.
Topics & Concepts
CrowdsComputer scienceConvolutional neural networkArtificial intelligenceArchitectureResidual neural networkDeep learningCrowdsourcingComputer visionMachine learningComputer securityGeographyWorld Wide WebArchaeologyAnomaly Detection Techniques and ApplicationsHuman Pose and Action RecognitionVideo Surveillance and Tracking Methods