Real Time Violence Detection and Monitoring System using Deep Learning
Abstract
Violence in the public places like fights, accidents, fires and chain snatching has become an alarming situation for the public safety and surveillance systems. Conventional surveillance methods heavily rely on continuous human surveillance which is time-consuming, inefficient, and delayed in response in critical situations. In this paper, we proposed an Intelligent Video Surveillance and Alert System (IVSAS) based on deep learning techniques for real-time violence detection and monitoring to overcome the above limitations. The proposed framework integrates YOLO-based object detection for violent incident identification and MobileNet-based feature extraction for effective spatial feature representation with reduced computational complexity. The system uses OpenCV for live streams of surveillance video and performs keyframe extraction, preprocessing, temporal analysis and confidence-based classification for better detection accuracy with low false positive rates. When violent activity is detected, an automated alert mechanism via the Telegram Bot API immediately sends alert messages and detected incident frames to authorised security personnel for rapid response. The results shows that the proposed system achieves detection accuracy over 90%, real-time processing performance and latency of alert generation. The proposed framework is an efficient, scalable and lightweight solution for real time public safety surveillance applications.