Automatic Gun Detection Approach for Video Surveillance

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Author(s): Mai Kamal el den Mohamed (Faculty of Computer and Informatics, Cairo, Egypt), Ahmed Taha (Faculty of Computer and Informatics, Cairo, Egypt)and Hala H. Zayed (Faculty of Computer and Informatics, Cairo, Egypt)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 18
Source title: International Journal of Sociotechnology and Knowledge Development (IJSKD)
Editor(s)-in-Chief: Lincoln Christopher Wood (University of Otago, New Zealand)and Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/IJSKD.2020010103

Keywords: Information Science Reference / Knowledge Society / Social Sciences & Humanities / Sociology

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Abstract

The immense crime rates resulting from using pistols have led governments to seek solutions to deal with such terrorist incidents. These incidents have a negative impact on public security and cause panic among citizens. From this point, facing a pandemic of weapon violence has become an important research topic. One way to reduce this kind of violence is to prevent it via remote detection and to give an appropriate response in a short time. Video surveillance is the process of monitoring the behavior of people and objects. Surveillance systems can be employed in security applications as legal evidence. Moreover, it is used widely in suspicious activity detection applications. Intelligent video surveillance systems (IVSSs) are the use of automatic video analytics to enhance the effectiveness of traditional surveillance systems. With the rapid development in Deep Learning (DL), it is now widely used to address the problems existing in traditional detection techniques. In this article, an approach to detect pistols and guns in video surveillance systems is proposed. The presented approach does not need any invasive tools in the weapon detection process. It uses DL in the classification and the detection processes. The proposed approach enhances the obtained results by applying Transfer Learning (TL). It employs two different DL techniques: AlexNet and GoogLeNet. Experimental results verify the adaptability of detecting different types of pistols and guns. The experiments were conducted on a benchmark gun database called Internet Movie Firearms Database (IMFDB). The results obtained suggest that the proposed approach is promising and outperforms its counterparts.

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