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학술지 Lightweight Driver Monitoring System Based on Multi-Task Mobilenets
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저자
김휘, 정우성, 최현균
발행일
201907
출처
Sensors, v.19 no.14, pp.1-18
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s19143200
초록
Research on driver status recognition has been actively conducted to reduce fatal crashes caused by the driver's distraction and drowsiness. As in many other research areas, deep-learning-based algorithms are showing excellent performance for driver status recognition. However, despite decades of research in the driver status recognition area, the visual image-based driver monitoring system has not been widely used in the automobile industry. This is because the system requires high-performance processors, as well as has a hierarchical structure in which each procedure is affected by an inaccuracy from the previous procedure. To avoid using a hierarchical structure, we propose a method using Mobilenets without the functions of face detection and tracking and show this method is enabled to recognize facial behaviors that indicate the driver's distraction. However, frames per second processed by Mobilenets with a Raspberry pi, one of the single-board computers, is not enough to recognize the driver status. To alleviate this problem, we propose a lightweight driver monitoring system using a resource sharing device in a vehicle (e.g., a driver's mobile phone). The proposed system is based on Multi-Task Mobilenets (MT-Mobilenets), which consists of the Mobilenets?? base and multi-task classifier. The three Softmax regressions of the multi-task classifier help one Mobilenets base recognize facial behaviors related to the driver status, such as distraction, fatigue, and drowsiness. The proposed system based on MT-Mobilenets improved the accuracy of the driver status recognition with Raspberry Pi by using one additional device.
키워드
Distraction, Driver assistance, Drowsiness, ECD, ECT, Fatigue, Lightweight, PERCLOS, Raspberry pi, SBC, Single-board computer
KSP 제안 키워드
Automobile industry, Driver Monitoring System, Driver status, Frames per second(FPS), High performance, Image-based, Learning-based algorithms, Single Board Computer, Status recognition, Visual image, deep learning(DL)
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