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Lightweight Driver Monitoring System Based on Multi-Task Mobilenets

Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3200;
Received: 12 June 2019 / Revised: 11 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
PDF [4375 KB, uploaded 20 July 2019]


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. View Full-Text
Keywords: lightweight; driver assistance; drowsiness; fatigue; distraction; PERCLOS; ECT; ECD; single-board computer; SBC; Raspberry pi lightweight; driver assistance; drowsiness; fatigue; distraction; PERCLOS; ECT; ECD; single-board computer; SBC; Raspberry pi

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Kim, W.; Jung, W.-S.; Choi, H.K. Lightweight Driver Monitoring System Based on Multi-Task Mobilenets. Sensors 2019, 19, 3200.

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