Real-time based Violence Detection from CCTV Camera using Machine Learning Method

Based on deep-learning approaches, we developed a real-time violence detector for surveillance video systems. In the model given here (overall generality-accuracy-fast response time), CNN serves as a space feature extractor, while LSTM is used to learn time-related relationships. Due to the large number of devices that can record video from camera systems, like those used in surveillance systems, body-worn webcams, and phones, it has become hard to keep track of video footage from many surveillance devices. Using crowd-based video footage, we analyzes and alerts possible those who are affected by violent material is found in the clip. Keeping an eye on huge crowds during social gatherings, especially those where there is a possibility of violence becomes very difficult. The speed, accuracy, and generality of violent event detectors across a variety of video sources and formats are all factors that go into determining their usefulness. Intelligent monitoring technology has been extensively deployed in the nation in recent years to continually support the development of a safe city. Behavioral intelligence analysis is becoming more popular in the realm of intelligent image analysis. Currently, complex activities such as fighting or violence are rarely studied in behavior analysis techniques; instead, they focus on basic movements such as running or leaping. To preserve social order and safeguard people's lives and property, competent and intelligent analysis of violence through video surveillance is vital. To that end, we've put up an overview of the most recent techniques for spotting violent scenes in recorded video.