Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences

The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive algorithm based facial landmarks the accuracy, which detects driver’s status by using video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network constructed detect face region 68 key points, can solve optimization problem caused different convergence speeds each task. According images, eye feature aspect ratio (EAR), mouth (MAR) and percentage closure time (PERCLOS) are calculated landmarks. assessment model established assess drivers through eye/mouth selection. After series comparative experiments, results show that this proposed achieves good performance in both accuracy speed for detection.