Real-Time Fire Detection in Surveillance Footage: Implementing a Robust Image Processing Framework

Real-time fire detection in surveillance footage is a critical component of modern safety and security systems.This paper presents a robust image processing framework designed to detect fire in real-time using surveillance cameras.The framework employs advanced techniques in image processing, computer vision, and machine learning to accurately identify fire characteristics such as color, shape, and motion.Key components of the framework include preprocessing steps to enhance image quality, feature extraction methods to isolate fire-like patterns, and classification algorithms to differentiate between fire and nonfire scenarios.The proposed system is evaluated using a comprehensive dataset of surveillance footage, demonstrating high accuracy, low false positive rates, and rapid detection times.This research highlights the potential of leveraging advanced image processing techniques for effective fire detection, contributing to enhanced safety measures and timely response in emergency situations.