Fire Detection Using CCTV Surveillance

This study investigates the development and implementation of a Real-Time Fire Detection System using Video Analytics, an advanced monitoring framework designed to enhance safety and surveillance through intelligent video-based analysis. The system utilizes live camera feeds as input and applies a combination of image processing and deep learning techniques to automatically detect fire in real time. The framework leverages Convolutional Neural Networks (CNN) for accurate classification and color-based image processing to identify fire-like regions based on visual characteristics such as color, intensity, and flickering behavior. The camera continuously captures video frames that are processed using OpenCV to extract color and motion features, which are then passed through the CNN model for precise classification. When fire is detected, the system highlights the affected region within the frame and immediately displays an alert message on the monitoring interface. This non-intrusive, sensor-free system offers a cost-effective, reliable, and efficient way to detect fire compared to traditional smoke or heat-based systems. It ensures rapid response and reduces the likelihood of false alarms by integrating deep learning-based classification with image analysis. The system is suitable for real-time applications in industrial areas, smart homes, public places, and surveillance systems, where early fire detection is crucial for preventing damage and ensuring safety.