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Automatic Gun Detection Approach for Video Surveillance

Automatic Gun Detection Approach for Video Surveillance

Mai Kamal el den Mohamed, Ahmed Taha, Hala H. Zayed
Copyright: © 2020 |Volume: 12 |Issue: 1 |Pages: 18
ISSN: 1941-6253|EISSN: 1941-6261|EISBN13: 9781799805670|DOI: 10.4018/IJSKD.2020010103
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MLA

el den Mohamed, Mai Kamal, et al. "Automatic Gun Detection Approach for Video Surveillance." IJSKD vol.12, no.1 2020: pp.49-66. http://doi.org/10.4018/IJSKD.2020010103

APA

el den Mohamed, M. K., Taha, A., & Zayed, H. H. (2020). Automatic Gun Detection Approach for Video Surveillance. International Journal of Sociotechnology and Knowledge Development (IJSKD), 12(1), 49-66. http://doi.org/10.4018/IJSKD.2020010103

Chicago

el den Mohamed, Mai Kamal, Ahmed Taha, and Hala H. Zayed. "Automatic Gun Detection Approach for Video Surveillance," International Journal of Sociotechnology and Knowledge Development (IJSKD) 12, no.1: 49-66. http://doi.org/10.4018/IJSKD.2020010103

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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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