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(to be completed)

 


ThinkMind // International Journal On Advances in Systems and Measurements, volume 5, numbers 3 and 4, 2012 // View article sysmea_v5_n34_2012_1


Weapon Detection and Classification Based on Time-Frequency Analysis of Electromagnetic Transient Images

Authors:
Abdalrahman Al-Qubaa
Gui Yun Tian

Keywords: electromagnetic imaging; weapon detection; feature extraction; airport security.

Abstract:
Terrorist groups, hijackers, and people hiding guns and knives are a constant and increasing threat. Concealed weapon detection has become one of the greatest challenges facing the law enforcement community today. The fact that most weapons are made from metallic materials makes electromagnetic detection methods the most prominent and preferred approach for concealed weapon detection. Each weapon has a unique electromagnetic fingerprint, determined by its size, shape and physical composition. A new detection system developed at Newcastle University that uses a walk-through metal detector with a Giant Magneto-Resistive sensor array has been utilized in this study. The system enables a two-dimensional image to be constructed from measured signals and used in later image processing. This paper addresses weapon detection using time and frequency feature extraction techniques based on this new system. The study also employs and compares two classification techniques for potential automated classification. Experimental results using guns and non-gun objects in controlled and non-controlled environments have demonstrated the potential and efficiency of the new system. The classification capabilities of the system could be developed to the point that individuals could pass through the system without the need to take off other metallic objects. The proposed techniques have the potential to produce major improvements in automatic weapon detection and classification.

Pages: 89 to 99

Copyright: Copyright (c) to authors, 2012. Used with permission.

Publication date: December 31, 2012

Published in: journal

ISSN: 1942-261x

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