Driver Fatigue Monitoring System Using Video Images and Steering Grip Force

DOI

10.2991/icmia-16.2016.111How to use a DOI?

Keywords

fatigue detection; Active Shape Models; grip force; fuzzy classifier.

Abstract

Previous research uses eye blink sensor technology to detect driver's fatigue, which has some limitations in the condition of wearing glasses and lighting changes. To overcome those problems, this paper proposes a way to detect fatigue for drivers through video images and grip forces on steering wheel. Simulated driving experiments are conducted on a platform developed by simulated driving software, during which grip forces of both hands and video images are collected. Analyzing the images from the video, we applied adaptive boosting (Adaboost) and Active Shape Models (ASM) algorithm to get the changes on the face, such as degrees of eye closure and degrees of mouth opening. These parameters including grip force that are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The results show that use of multiple visual parameters combined with steering grip force can effectively detect the driver's fatigue.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun Zhao
AU  - Kuangrong Hao
AU  - Yongsheng Ding
PY  - 2016/11
DA  - 2016/11
TI  - Driver Fatigue Monitoring System Using Video Images and Steering Grip Force
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.111
DO  - 10.2991/icmia-16.2016.111
ID  - Zhao2016/11
ER  -