Driver Fatigue Monitoring System Using Video Images and Steering Grip Force
- 10.2991/icmia-16.2016.111How to use a DOI?
- fatigue detection; Active Shape Models; grip force; fuzzy classifier.
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.
- © 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/).
Cite this article
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 -