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http://dx.doi.org/10.5909/JBE.2018.23.6.780

Armed person detection using Deep Learning  

Kim, Geonuk (Department of Electronic Engineering, Kwangwoon University)
Lee, Minhun (Department of Electronic Engineering, Kwangwoon University)
Huh, Yoojin (Department of Electronic Engineering, Kwangwoon University)
Hwang, Gisu (Department of Electronic Engineering, Kwangwoon University)
Oh, Seoung-Jun (Department of Electronic Engineering, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.23, no.6, 2018 , pp. 780-789 More about this Journal
Abstract
Nowadays, gun crimes occur very frequently not only in public places but in alleyways around the world. In particular, it is essential to detect a person armed by a pistol to prevent those crimes since small guns, such as pistols, are often used for those crimes. Because conventional works for armed person detection have treated an armed person as a single object in an input image, their accuracy is very low. The reason for the low accuracy comes from the fact that the gunman is treated as a single object although the pistol is a relatively much smaller object than the person. To solve this problem, we propose a novel algorithm called APDA(Armed Person Detection Algorithm). APDA detects the armed person using in a post-processing the positions of both wrists and the pistol achieved by the CNN-based human body feature detection model and the pistol detection model, respectively. We show that APDA can provide both 46.3% better recall and 14.04% better precision than SSD-MobileNet.
Keywords
Object-related human detection; Pose estimation; Object detection; CNN; Deep learning;
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Times Cited By KSCI : 3  (Citation Analysis)
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