Research on driver fatigue detection

Ting Zhang; Zhong Chen; Chao Ouyang

doi:10.1117/12.2285585

8 March 2018 Research on driver fatigue detection

Ting Zhang, Zhong Chen, Chao Ouyang

Author Affiliations +

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060918 (2018) https://doi.org/10.1117/12.2285585
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Driver fatigue is one of the main causes of frequent traffic accidents. In this case, driver fatigue detection system has very important significance in avoiding traffic accidents. This paper presents a real-time method based on fusion of multiple facial features, including eye closure, yawn and head movement. The eye state is classified as being open or closed by a linear SVM classifier trained using HOG features of the detected eye. The mouth state is determined according to the width-height ratio of the mouth. The head movement is detected by head pitch angle calculated by facial landmark. The driver’s fatigue state can be reasoned by the model trained by above features. According to experimental results, drive fatigue detection obtains an excellent performance. It indicates that the developed method is valuable for the application of avoiding traffic accidents caused by driver’s fatigue.

Citation Download Citation

Ting Zhang, Zhong Chen, and Chao Ouyang "Research on driver fatigue detection", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060918 (8 March 2018); https://doi.org/10.1117/12.2285585

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