Gun and Knife Detection Based on Faster R-CNN for Video Surveillance

Public safety in public areas is nowadays one of the main concerns for governments and companies around the world. Video surveillance systems can take advantage from the emerging techniques of deep learning to improve their performance and accuracy detecting possible threats. This paper presents a system for gun and knife detection based on the Faster R-CNN methodology. Two approaches have been compared taking as CNN base a GoogleNet and a SqueezeNet architecture respectively. The best result for gun detection was obtained using a SqueezeNet architecture achieving a 85.44% AP(50). For knife detection, the GoogleNet approach achieved a 46.68% AP(50). Both results improve upon previous literature results evidencing the effectiveness of our detectors.