Research on fatigue driving detection methods

Ge Qin; Jingsheng Wang

doi:10.1117/12.2658140

22 December 2022 Research on fatigue driving detection methods

Ge Qin, Jingsheng Wang

Proceedings Volume 12460, International Conference on Smart Transportation and City Engineering (STCE 2022); 124602G (2022) https://doi.org/10.1117/12.2658140
Event: International Conference on Smart Transportation and City Engineering (STCE 2022), 2022, Chongqing, China

Abstract

Under the background of increasing car ownership and frequent traffic accidents, this paper focuses on fatigue driving, an important cause of traffic accidents, and mainly discusses the detection method of driver fatigue driving. This paper first sorts out the traditional subjective and objective detection indicators and judgment standards for fatigue driving, analyzes the advantages and disadvantages of the traditional detection methods, and lists the commonly used public data sets; At the same time, this paper further summarizes the commonly used driver facial feature recognition and extraction methods, list new fatigue driving detection methods based on machine learning and deep learning to improve the shortcomings of traditional detection and improve detection accuracy, and finally summarize and prospect the fatigue driving detection technology. The research believes that fatigue driving detection methods based on deep learning are the general trend, which can achieve high-precision, real-time and fast fatigue detection.

Citation Download Citation

Ge Qin and Jingsheng Wang "Research on fatigue driving detection methods", Proc. SPIE 12460, International Conference on Smart Transportation and City Engineering (STCE 2022), 124602G (22 December 2022); https://doi.org/10.1117/12.2658140

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KEYWORDS

Feature extraction

Convolutional neural networks

Mouth

Video

Eye

Neural networks

Electrocardiography

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