OpenCV-Based Real-Time Multi-Modal Threat Detection and Alert System for Public Safety Applications

The introduction of efficient systems which can respond quickly and accurately will result in the safety of the people in crowded or dangerous places. A real-time Threat Detection and Alert System is presented in this paper, which is implemented using OpenCV and utilizes computer vision and deep learning to recognize hazards such as guns, fights, or flames. The intention is to create a system that can ’see’, ’hear’ and ’move’ just like a human being to understand its environment. Live video will be taken using OpenCV, and then every frame will be processed to focus the attention on the important parts of the scene. While the YOLOv8 deep learning model identifies weapons, the 3D Convolutional Neural Networks (3D-CNNs) monitor human’s gestures to classify actions as Fighting or Aggressive. An audio-based CNN will similarly be listening for shocking sounds—screams or gunshots—for instance—to corroborate the camera’s detection. The combination of all these inputs is done through a weighted Decision model which gives a single score indicating the likelihood of a threat’s presence. The system will immediately trigger a loud alarm sound and display a red warning on the screen if this score goes beyond a predetermined limit, thus instantly notifying the closest people to either evacuate or stay safely. The tests carried out in real-life situations showed that the system is very effective, with high accuracy and almost no delay. It is rapid, reliable, and easy to implement, thus making it ideal for locations such as public surveillance systems, train stations, schools, or even smart city infrastructure. By combining visual, movement, and sound analysis, this system offers an intelligent and responsive Approach to public safety beyond previous capabilities.