Authors - R Suganya, S Priya, Sheeja Pon Chakravarthy,Pragadheesh Thirumal M
Abstract - This paper presents a real-time intelligent surveillance system designed to detect weapons and violent activities using deep learning techniques [2]. The system integrates the YOLOv7 object detection model [7] for weapon recognition and a CNN-based violence detection module for behavioral analysis. Real-time video streams from CCTV cameras are processed to identify potential threats, and alerts are transmitted via MQTT for immediate notification. Experimental evaluation demonstrates that the YOLOv7 model achieves a mean Average Precision (
[email protected]) of 55.3% for weapon detection, while the CNN model [11] attains 96% accuracy in classifying violent actions. The system operates at an average speed of 25–30 frames per second with low latency, confirming its feasibility for live surveillance applications. The proposed architecture enhances public safety by providing automated, accurate, and real-time monitoring capabilities.
