A Gun Detection Dataset and Searching for Embedded Device Solutions
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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 www.linksprite.com/gun-detection-datasets. 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.
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