A Comprehensive Review on Vision-Based Violence Detection in Surveillance Videos

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Abstract

Recent advancements in intelligent surveillance systems for video analysis have been a topic of great interest in the research community due to the vast number of applications to monitor humans’ activities. The growing demand for these systems aims towards automatic violence detection (VD) systems enhancing and comforting human lives through artificial neural networks (ANN) and machine intelligence. Extremely overcrowded regions such as subways, public streets, banks, and the industries need such automatic VD system to ensure safety and security in the smart city. For this purpose, researchers have published extensive VD literature in the form of surveys, proposals, and extensive reviews. Existing VD surveys are limited to a single domain of study, i.e., coverage of VD for non-surveillance or for person-to-person data only. To deeply examine and contribute to the VD arena, we survey and analyze the VD literature into a single platform that highlights the working flow of VD in terms of machine learning strategies, neural networks (NNs)-based patterns analysis, limitations in existing VD articles, and their source details. Further, we investigate VD in terms of surveillance datasets and VD applications and debate on the challenges faced by researchers using these datasets. We comprehensively discuss the evaluation strategies and metrics for VD methods. Finally, we emphasize the recommendations in future research guidelines of VD that aid this arena with respect to trending research endeavors.

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