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 occasions, while also performing the violence detection activities in (near) real-time.

This paper presents VERGE, an interactive video search engine that integrates multiple retrieval methodologies and also combines them with reranking and fusion techniques. Moreover, a user interface, implemented as a Web application, enables users to formulate queries, view the top retrieved shots and watch the respective videos, before submitting a shot to a VBS task, all in an efficient and easy manner.

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