Real-Time Violence Detection and Instant Alert System Using Deep Learning and Computer Vision

Abstract

Traditional CCTV surveillance systems primarily record events, making it difficult for authorities to respond promptly to incidents of violence. This paper presents a real-time violence detection and instant alert system that leverages deep learning and computer vision techniques to identify violent activities such as fights, assaults, and aggressive behaviour from live video streams. The proposed system integrates convolutional neural networks (CNN) and long short-term memory (LSTM) models (or YOLO based architectures) to accurately detect violent actions in complex environments. Upon detecting suspicious activity, the system triggers automated alerts via SMS, email, or mobile application notifications to concerned authorities, along with the incident’s location and video snippet. This solution can be deployed in public spaces, educational institutions, workplaces, malls, airports, and smart city surveillance to enhance safety and enable rapid response during emergencies. By combining machine learning, real-time video processing, and alert mechanisms, the proposed framework offers a proactive and cost-effective approach to improving public security and reducing response time.