Proceedings of the Multimedia University Engineering Conference (MECON 2022)

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Weapon Detection in Surveillance Videos Using Deep Neural Networks

Authors
Muhammad Ekmal Eiman Quyyum1, *, Mohd Haris Lye Abdullah1, *
1Faculty of Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia
*Corresponding author. Email: 1171102157@student.mmu.edu.my
*Corresponding author. Email: haris.lye@mmu.edu.my
Corresponding Authors
Muhammad Ekmal Eiman Quyyum, Mohd Haris Lye Abdullah
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-082-4_19How to use a DOI?
Keywords
Deep neural network; Artificial Intelligence; Surveillance video; weapon detection
Abstract

Object detection uses computer vision technique to identify and locate objects in an image or video. This feature can help to improve the security level as it can be deployed to detect a dangerous weapon with object detection methods. Driven by the success of deep learning methods, this study aims to develop and evaluate the use the deep neural network for weapon detection in surveillance videos. The YOLOv3 with Darknet-53 as feature extractor is used for detecting two types of weapons namely pistol and knife. The YOLOv3 Darknet-53 is further improved by optimizing the network backbone. This is achieved by adding a fourth prediction layer and customizing the anchor boxes in order to detect the smaller objects. The proposed model is evaluated with the Sohas weapon detection dataset. The performance of the model is evaluated in terms of precision, recall, mean average precision (mAP) and detection speed in frame per second (FPS).

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Multimedia University Engineering Conference (MECON 2022)
Series
Advances in Engineering Research
Publication Date
23 December 2022
ISBN
10.2991/978-94-6463-082-4_19
ISSN
2352-5401
DOI
10.2991/978-94-6463-082-4_19How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Muhammad Ekmal Eiman Quyyum
AU  - Mohd Haris Lye Abdullah
PY  - 2022
DA  - 2022/12/23
TI  - Weapon Detection in Surveillance Videos Using Deep Neural Networks
BT  - Proceedings of the Multimedia University Engineering Conference (MECON 2022)
PB  - Atlantis Press
SP  - 183
EP  - 195
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-082-4_19
DO  - 10.2991/978-94-6463-082-4_19
ID  - Quyyum2022
ER  -