Real Time Violence Detection and Alert System Using Camera and GSM

Security and surveillance systems play a crucial role in maintaining public safety, yet traditional monitoring methods relying on human supervision can be inefficient and prone to errors. To address this challenge, this project presents a real-time violence detection and alert system that automates the identification of violent activities using machine learning-based video analysis. The system captures live video footage through a camera and processes it using deep learning algorithms such as CNN or LSTM to detect aggressive behavior based on motion patterns and human postures. Once violence is detected, an alert signal is sent via serial communication to an Arduino microcontroller, which then triggers a GSM module to send an SMS notification to security personnel or authorities for immediate intervention. This system offers several advantages, including real-time automated detection, eliminating the need for constant human surveillance. The integration of deep learning models enhances accuracy in recognizing violent activities, reducing false alarms. The use of Arduino and GSM for alert transmission ensures a fast and reliable communication channel without requiring an internet connection, making it suitable for deployment in remote or high-risk areas. By combining AI-driven video processing with hardware-based communication, the proposed system provides an efficient, real-time solution for violence detection and security enhancement.