Detection of Human in Flames Using HOG & SVM

Abstract-This project is designed to assist in locating individuals trapped in fire emergencies by integrating two interconnected components: fire detection and human identification. The system employs the YCbCr color space standard to detect fire and flames within the environment. To identify individuals amidst the fire, it leverages the HOG combined with a SVM classifier. Motion-based feature selection techniques are utilized for human activity recognition in video sequences. To ensure seamless operation of both modules, they are systematically integrated. Fire detection is carried out using a trained model that incorporates a diverse range of human feature sets. Additionally, moving objects are identified using a combination of a color median filter and background differencing, following four distinct rules. A critical aspect of this approach is the dependency between fire detection and human identification—ensuring that if a fire is detected, the system actively searches for trapped individuals. The primary objective of this methodology is to enhance the efficiency of locating individuals in hazardous fire conditions, enabling rapid rescue operations. This system can support firefighters in strategic decision-making and identifying high-risk zones.