Intelligent in-vehicle safety and security monitoring system



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Dangerous situations such as children are left in vehicles, are dropped off at wrong stops, or take on wrong school buses usually caused by the negligence of drivers. This paper presents a real-time intelligent in-vehicle monitoring system that can count and recognize people as well as alert drivers if such improprieties or potential dangers happen. The system uses an HOG-based face detector from the Dlib library to obtain face counting function. Face recognition is achieved through two steps, facial feature extraction, and face identification. The ResNet is used in facial feature extraction. It transforms an aligned face into a 256-dimensional vector, a Euclidean facial embedding. In face identification, labeled faces will be transformed into facial embeddings first. Then k-nearest neighbor classifier (kNN) is adopted to identify people using such facial embeddings. The simulation on ChokePoint dataset is tested and the average accuracy is 93 percent. The distance sensor performs well when it is installed 100 cm in front of people. Whether the motion sensor is installed depends on special conditions.



real-time face recognition, feature extraction, face classification, Raspberry Pi, distance sensor, motion sensor