Controlling Driver Behaviour in ADAS with Emotions Recognition System

Abstract

The work focuses on the development of a non-intrusive driver behaviour tracking system. This system can be used in Advanced Driver Assistance Systems (ADAS). The principle of the developed algorithm is to apply the ViolaJones method to determine human emotions. By capturing an image and performing its integral representation, the method performs video stream processing based on Haar-like features designed to find an identifiable object. By applying AdaBoost machine learning method, which sifts out low-informative features and creates strong classifiers from a set of weak ones, the algorithm recognises the driver's face, estimates his age and emotions in real-time and sends a warning signal to the user based on the received data. The paper demonstrates the effectiveness of the ViolaJones algorithm to develop a computer vision system for identifying and tracking a person's state and emotions. Combining the results, an ADAS-like system for tracking a driver while driving has been developed. Through simulations, the high accuracy and speed of the algorithm for determining the emotion and age of the driver has been demonstrated. The synthesised algorithm can be used to analyse driver behaviour while driving.