Estudo de viabilidade de sistemas de detecção de armamentos em tempo real em linhas de ônibus urbanos

Abstract

Surveillance systems are fundamental on preventing armed robberys on public busses. However, to be operated in real-time theses systems demand an unrealistic amount of people. The usage of computer vision and deep learning technics raises as a way to automate parts or even the whole surveillance process, from the weapons detection to the alarm triggering. For this process to be accomplished efficiently, allowing authorities to take more effective actions, the system needs to be able to handle a growing security cameras demand. Thus, this work analyses a bus line weapon detection system viabillity. Through simulation, this work evaluated the perfomance of YOLO algorithm, in its fourth version, on a client-server model under a growing security camera demand. The server is composed of a Tesla V80 GPU with a 12GB memory, Intel Xeon dual core processor, 61GB RAM memory and 200GB disk space. Finally, from the gathered results, its observable that the application presents a detection time increase after having introduced 16 virtual users (cameras), also the average response time cannot be considered as real-time, on bus security context.