A Multi-view Driver Drowsiness Detection Method Using Transfer Learning and Population-based Sampling Strategy

Chen, Jinxin; Fang, Zhenwu; Wang, Jinxiang; Chen, Jiansong; Yin, Guodong

doi:10.1109/itsc55140.2022.9922476

Cited by 2 publications

References 21 publications

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Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition

Kim,

Park,

Kim

et al. 2023

Sci Rep

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This paper introduces a real-time Driver Monitoring System (DMS) designed to monitor driver behavior while driving, employing facial landmark estimation-based behavior recognition. The system utilizes an infrared (IR) camera to capture and analyze video data. Through facial landmark estimation, crucial information about the driver’s head posture and eye area is extracted from the detected facial region, obtained via face detection. The proposed method consists of two distinct modules, each focused on recognizing specific behaviors. The first module employs head pose analysis to detect instances of inattention. By monitoring the driver’s head movements along the horizontal and vertical axes, this module assesses the driver’s attention level. The second module implements an eye-closure recognition filter to identify instances of drowsiness. Depending on the continuity of eye closures, the system categorizes them as either occasional drowsiness or sustained drowsiness. The advantages of the proposed method lie in its efficiency and real-time capabilities, as it solely relies on IR camera video for computation and analysis. To assess its performance, the system underwent evaluation using IR-Datasets, demonstrating its effectiveness in monitoring and recognizing driver behavior accurately. The presented real-time Driver Monitoring System with facial landmark-based behavior recognition offers a practical and robust approach to enhance driver safety and alertness during their journeys.

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