VisionGuard: Deep Learning–Based Weapon Detection Framework

Abstract: We have a lot of surveillance systems in places and private areas these days. This means we really need to have systems that can find threats right away. Usually people watch the cameras. We use simple rules to figure out what is going on. This does not work very well when there are a lot of people around and things get complicated. This paper is about a surveillance system that uses artificial intelligence and looks at pictures to find threats, in real time and understand what is happening. The system uses deep learning techniques to do this. The system they are talking about uses a Swin Transformer to find objects like weapons or unattended bags. It can also detect when someone is not supposed to be in an area. They also have a model that looks at how people move to see if they are doing something weird. This model uses something called Graph Convolutional Networks. It can tell if someone is just hanging around being violent or moving in a way. The Swin Transformer and the movement model work together to make sure the system is accurate and does not send out a lot of warnings. The system is really good at finding objects and strange movements, like the Swin Transformer finding weapons and the movement model finding unusual movement patterns. When we find something that could be a problem the system sends out alerts to help the security team act fast. The tests we did show that this way of doing things works well even in tough situations, which means it is a good choice for new smart surveillance systems that are being developed.

Keywords: Surveillance Systems, Threat Detection, Computer Vision, Deep Learning, Swin Transformer, Graph Convolutional Networks, Situational Awareness