Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

Abstract

Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. The traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. The purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. The goal is to alarm and alert the security officials to take proactive action to prevent violent activities. In deep learning literature, region-based classifiers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classifiers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. The method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by filtering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70–30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. This hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.