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JBE, vol. 23, no. 6, pp.780-789, November, 2018


Armed person detection using Deep Learning

Geonuk Kim, Minhun Lee, Yoojin Huh, Gisu Hwang, and Seoung-Jun Oh

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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.

Keyword: Object-related human detection, Pose estimation, Object detection, CNN, Deep learning

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