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A Gun Detection Dataset and Searching for Embedded Device Solutions

2021-05-03 17:58:45
Delong Qi, Weijun Tan, Zhifu Liu, Qi Yao, Jingfeng Liu
arXiv_CV
         
Abstract

Gun violence is a severe problem in the world, particularly in the United States. Computer vision methods have been studied to detect guns in surveillance video cameras or smart IP cameras and to send a real-time alert to safety personals. However, due to no public datasets, it is hard to benchmark how well such methods work in real applications. In this paper we publish a dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification we collect from a few different sources. To our knowledge, this is the largest dataset for the study of gun detection. This dataset can be downloaded at this http URL. We also study to search for solutions for gun detection in embedded edge device (camera) and a gun/non-gun classification on a cloud server. This edge/cloud framework makes possible the deployment of gun detection in the real world.

Abstract (translated)

URL

https://arxiv.org/abs/2105.01058

PDF

https://arxiv.org/pdf/2105.01058.pdf

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