A Dataset and System for Real-Time Gun Detection in Surveillance Video Using Deep Learning
This addresses gun violence by enabling real-time alerts for security personnel, but it is incremental as it builds on existing deep learning methods with a new dataset.
The authors tackled the problem of gun detection in surveillance video by publishing a large dataset of 51K annotated gun images and developing a real-time system using an edge/cloud framework, achieving deployment feasibility.
Gun violence is a severe problem in the world, particularly in the United States. Deep learning methods have been studied to detect guns in surveillance video cameras or smart IP cameras and to send a real-time alert to security personals. One problem for the development of gun detection algorithms is the lack of large public datasets. In this work, we first 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 present a gun detection system using a smart IP camera as an embedded edge device, and a cloud server as a manager for device, data, alert, and to further reduce the false positive rate. We study to find solutions for gun detection in an embedded device, and for gun classification on the edge device and the cloud server. This edge/cloud framework makes the deployment of gun detection in the real world possible.