Paper Key : IRJ************939
Author: Mohammad Roshan Ara,Manikar Vaidehi,Matta Sravanthi,B.pavani
Date Published: 02 Mar 2024
Abstract
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring.In this project it implements automatic gun (or) weapon detection using deep learning techniques like convolution neural network (CNN) based SSD, Faster RCNN algorithms and mainly YOLOV3 algorithm. Leveraging the YOLOv3 model's real-time processing capabilities and high accuracy, our system achieves remarkable performance in detecting weapons. Through frame-by-frame analysis, this system ensures comprehensive coverage, allowing for precise detection of weapons in dynamic environments. Moreover, we extend our system to support live video streams, enabling real-time weapon detection for enhanced security monitoring.Key Words: YOLOV3, weapon detection, Convolution Neural Network (CNN), Faster Region based Convolution Neural Network (RCNN), Single Shot Detection (SSD).