Driver Dangerous State Detection Based on OpenCV & Dlib Libraries Using Mobile Video Processing

The real-time driving behavior monitoring plays a significant role in intelligent transportation systems. Such monitoring increases traffic safety by reducing and eliminating the risk of potential traffic accidents. Vision-based approach including video cameras for dangerous situation detection is undoubtedly one of the most perspective and commonly used in sensing driver environment. In this case, the images of a driver, captured with video cameras, can describe its facial features, like head movements, eye state, mouth state, and, afterwards, identify a current level of fatigue state. In this paper, we leverage the built-in front-facing camera of the smartphone to continuously track driving facial features and early recognize driver's drowsiness and distraction dangerous states. Dangerous state recognition is classified into online and offline modes. Due to efficiency and performance of smartphones in online mode, the driving dangerous states are determined in real time on the mobile devices with aid of computer vision libraries OpenCV and Dlib while driving. Otherwise, the offline mode is based on the results of statistical analysis provided by a cloud service, utilizing not only the accumulated statistics in real time, but also the previously collected, stored and produced by machine learning tools.