Fatigue State Detection From Multi-features
- 10.2991/icsnce-18.2018.47How to use a DOI?
- Fatigue Detection; Face Detection; Active Shape Model; Support Vector Machine
With the quickening pace of modern life and the increasing of work pressure, accidents caused by fatigue problems occur more and more frequently. Developing a high-performance fatigue monitoring technology can not only improve the driver's work efficiency, but also solve the security risks caused by fatigue driving. This paper presents an algorithm of fatigue state detection from multi-features, which can determine whether a driver is in a state of fatigue. The thesis focuses on a non-contact, real-time fatigue detection method based on video, and proposes an algorithm with multiple fatigue characteristics. Firstly, it collects the video through the camera and carries out simple preprocessing. Then, the face area is quickly located by AdaBoost and the face shape model is constructed by ASM, which is used for locating the eye and mouth precisely, and extracting the relevant parameters. Based on the above indicators, it establishes the mapping relation between the characteristic space and fatigue space to judge the status with the SVM. Experiment results show the efficiency of the proposed method.
- © 2018, 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 - Gao Yuan AU - Wang Changyuan PY - 2018/04 DA - 2018/04 TI - Fatigue State Detection From Multi-features BT - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018) PB - Atlantis Press SP - 234 EP - 237 SN - 2352-538X UR - https://doi.org/10.2991/icsnce-18.2018.47 DO - 10.2991/icsnce-18.2018.47 ID - Yuan2018/04 ER -