Human Violence Detection Using Deep Learning Techniques

Abstract

The world’s average annual fatality rate from human violence is 7.9 per 10,000 people. Most of this human violence takes place in an isolated area or of sudden. The information delay here is a major impediment in stopping these acts. To thrive on this issue, the detection technique is used in this study. Detecting moving objects from CCTV is one of the most effective computer vision algorithms. CCTV cameras are now in every streets which are extremely helpful in solving cases. Some techniques of deep learning are used as computer vision to predict and detect the action, properties from video. In real-time police reach violent destinations and start checking CCTV cameras, and investigate to proceed further. This study is deliberately designed to detect violent acts from CCTV cameras. The Inception – v3 and Yolo – v5 models detect the violent act, the number of persons involved, and also the weapons used in the situation. The study consists of these deep learning models, which are used to form a video detection system. This model can be used in real-time as an application programming interface (API) or software. The study results showed the proposed model achieves an accuracy of 74%.