Weapon Detection in Real-Time CCTV Videos Using Deep Learning

Abstract

Security and safety is a big concern for today's modern world. For a country to be economically strong, it must ensure a safe and secure environment for investors and tourists. Having said that, Closed Circuit Television (CCTV) cameras are being used for surveillance and to monitor activities i.e. robberies but these cameras still require human supervision and intervention. We need a system that can automatically detect these illegal activities. Despite state-of-the-art deep learning algorithms, fast processing hardware, and advanced CCTV cameras, weapon detection in real-time is still a serious challenge. Observing angle differences, occlusions by the carrier of the firearm and persons around it further enhances the difficulty of the challenge. This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as the reference class and relevant confusion objects inclusion concept is introduced to reduce false positives and false negatives. No standard dataset was available for real-time scenario so we made our own dataset by making weapon photos from our own camera, manually collected images from internet, extracted data from YouTube CCTV videos, through GitHub repositories, data by university of Granada and Internet Movies Firearms Database (IMFDB) imfdb.org. Two approaches are used i.e. sliding window/classification and region proposal/object detection. Some of the algorithms used are VGG16, Inception-V3, Inception-ResnetV2, SSDMobileNetV1, Faster-RCNN Inception-ResnetV2 (FRIRv2), YOLOv3, and YOLOv4. Precision and recall count the most rather than accuracy when object detection is performed so these entire algorithms were tested in terms of them. Yolov4 stands out best amongst all other algorithms and gave a F1-score of 91% along with a mean average precision of 91.73% higher than previously achieved.

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

“…Bhatti et al [ 30 ] constructed a customized dataset for training the YOLOv4 model and compared the results with the state-of-the-art methodology. They achieved a satisfactory result by testing their methods on a few videos.…”

Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems

“…Several articles [ 5 , 6 , 7 ] have discussed the problem of weapon detection in surveillance videos and how it is very important to automate such a process, since it will reduce the huge efforts needed to manually review the video streaming, reduce the violation of privacy, and most importantly provide a very fast response that may lead to crime prevention. Finding important scenes in video surveillance is a very important aspect when aiming to trigger certain events according to the content of such frames.…”

Weapon Detection in Surveillance Videos Using Human Inspired Particle Swarm Optimization Algorithm and Support Vector Machine

“…The rapid and precise detection of weapons in public places is necessary to mitigate or avoid risks. In this application, the closed-circuit television is widely used for recognizing dangerous situations, where the closed-circuit television is considered as the effective operational requirement in terms of safety aspects [1][2][3]. The main purpose of closed circuit television is to provide security, crime investigation, deterrence, and reduction in insurance costs [4,5].…”