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Gun violence is a severe problem in the world, particularly in the UnitedStates. Deep learning methods have been studied to detect guns in surveillancevideo cameras or smart IP cameras and to send a real-time alert to securitypersonals. One problem for the development of gun detection algorithms is thelack of large public datasets. In this work, we first publish a dataset with51K annotated gun images for gun detection and other 51K cropped gun chipimages for gun classification we collect from a few different sources. To ourknowledge, this is the largest dataset for the study of gun detection. Thisdataset can be downloaded at www.linksprite.com/gun-detection-datasets. Wepresent a gun detection system using a smart IP camera as an embedded edgedevice, and a cloud server as a manager for device, data, alert, and to furtherreduce the false positive rate. We study to find solutions for gun detection inan embedded device, and for gun classification on the edge device and the cloudserver. This edge/cloud framework makes the deployment of gun detection in thereal world possible.

A Dataset and System for Real-Time Gun Detection in Surveillance Video Using Deep Learning