Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment

Abstract

Gaze tracking is crucial for studying driver’s attention, detecting fatigue, and improving driver assistance systems, but it is difficult in natural driving environments due to nonuniform and highly variable illumination and large head movements. Traditional calibrations that require subjects to follow calibrators are very cumbersome to be implemented in daily driving situations. A new automatic calibration method, based on a single camera for determining the head orientation and which utilizes the side mirrors, the rear-view mirror, the instrument board, and different zones in the windshield as calibration points, is presented in this paper. Supported by a self-learning algorithm, the system tracks the head and categorizes the head pose in 12 gaze zones based on facial features. The particle filter is used to estimate the head pose to obtain an accurate gaze zone by updating the calibration parameters. Experimental results show that, after several hours of driving, the automatic calibration method without driver’s corporation can achieve the same accuracy as a manual calibration method. The mean error of estimated eye gazes was less than 5°in day and night driving.

A 3D Driver Head Pose Estimation Method Based on Depth Image

“…In this paper, gaze zones are similar to but less than our previous work [3]. Nine different gaze zones are the two side mirrors, the rear-view mirror, the dashboard, the console, the co-driver glove box and left three zones on the windshield, as shown in Figure 2.…”

“…For the real driving conditions, we improve the gaze zone partitioned method in our prior work [3]. Different gaze zones correspond to different head poses.…”

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

“…The authors trained gaze classifiers in a supervised framework to determine 18 gaze zones. Fu et al designed a system that categorizes the head pose into 12 different gaze zones based on facial features [23]. The system automatically learns the zones based on different calibration points, such as side mirrors, rearview mirrors, etc.…”

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

“…The first category is not suitable for trains because they use a track [23]. The second category analyses the changes of driver behaviour, such as eye tracking, yawning, percent eye closure (PERCLOS), blink frequency, nodding frequency, facial position, and inclination of the driver’s head [24,25,26,27,28]. Recent progress in machine vision research and advances in computer hardware technologies have made it possible to measure eyelid movement, face expression and head pose using video cameras.…”