Real-Time CCTV Video Analysis: Deep Learning for Weapon Detection

Abstract

CCTV cameras, you know, those surveillance cameras you often see in public places, stores, and important buildings, play a crucial role in keeping us safe and secure. They constantly record video footage to monitor what's going on around and ensure our safety. However, as safety concerns grow, it's becoming increasingly important to improve these CCTV systems, making them capable of detecting potential threats, like weapons, in real-time. The current way CCTV surveillance works is that human operators must manually watch all those live video feeds from multiple cameras, which is error-prone, and honestly, a person can only effectively keep an eye on so many cameras at once. With the huge amount of video data these cameras generate, it's practically impossible for human operators to watch everything continuously, which means security threats might get missed. The traditional approach is passive i.e., the security personnel or operators sit and watch the video feeds, hoping to spot anything suspicious, like someone carrying a weapon. But this approach has its limitations too. Humans can make mistakes, and they might not react quickly enough in a real-time situation. In addition, as the number of cameras increases, it becomes tough to scale this method, and the costs can go up significantly. So, to overcome these challenges and enhance public safety, a more advanced solution is required. Therefore, this project develops a real-time CCTV video analysis with deep learning for weapon detection. By using deep learning models, a sophisticated system can be built that quickly analyzes the video streams from CCTV cameras in real-time. This means it can detect weapons and potential threats as they happen. It's super-fast and accurate, so it reduces the chances of false alarms or missing something important. In addition, it's scalable, cost-effective and helps security agencies respond quickly to potential threats and keeps us all protected in a better and more efficient way.