Gun Detection in CCTV Images using HAAR-Like Features

Automated video-based surveillance is an important area of research to assist the security personnel to detect the incident of any abnormal events in the surroundings. The objective of this paper is to develop a framework for automatic gun detection using closed-circuit television (CCTV) images. The methodology presented in this paper involves the development of a framework for automatic gun detection using closed-circuit television (CCTV) images, with the aim of enhancing the surveillance of crime and improving human security. The proposed approach consists of a dataset of CCTV images containing instances of guns, as well as non-gun images for comparison. These images would be used to train the proposed algorithm to recognize and identify guns in future CCTV images. The proposed framework is designed for an indoor environment and uses Haar-like features for gun detection. The proposed system involves the installation of CCTV cameras in a suitable corner of an indoor environment for surveillance. The CCTV cameras capture the scene and the frames of the scene are compared with a predefined dataset for automatic gun detection. The proposed approach draws a bounding box and raises an alarm if it detects a gun in a frame extracted from a captured scene. This provides a visual indication of the presence of a gun, making it easier for relevant authorities to quickly identify and respond to the threat. The proposed system shows promising results in real-time applications and about 90% accuracy has been achieved.