Biblio

Security cameras and video surveillance systems have become important infrastructures for ensuring safety and security of the general public. However, the detection of high-risk situations through these systems are still performed manually in many cities. The lack of manpower in the security sector and limited performance of human may result in undetected dangers or delay in detecting threats, posing risks for the public. In response, various parties have developed real-time and automated solutions for identifying risks based on surveillance videos. The aim of this work is to develop a low-cost, efficient, and artificial intelligence-based solution for the real-time detection and recognition of weapons in surveillance videos under different scenarios. The system was developed based on Tensorflow and preliminarily tested with a 294-second video which showed 7 weapons within 5 categories, including handgun, shotgun, automatic rifle, sniper rifle, and submachine gun. At the intersection over union (IoU) value of 0.50 and 0.75, the system achieved a precision of 0.8524 and 0.7006, respectively.