Design of anti-fatigue driving system based on image recognition

Jing Yin; Jing Zhang; Xianlin Cao; Shunyu Yao; Xueying Hu

doi:10.1117/12.2644261

9 September 2022 Design of anti-fatigue driving system based on image recognition

Jing Yin, Jing Zhang, Xianlin Cao, Shunyu Yao, Xueying Hu

Proceedings Volume 12328, Second International Conference on Optics and Image Processing (ICOIP 2022); 123281M (2022) https://doi.org/10.1117/12.2644261
Event: Second International Conference on Optics and Image Processing (ICOIP 2022), 2022, Taian, China

Abstract

This design is an anti-fatigue driving system based on image recognition, using Raspberry Pi as the core processor. The image acquired by the video image acquisition unit is processed by the optimized Imutils library, and the model of the machine learning library Dlib is used to locate the key points, extract the driver's facial features, and calculate the eye aspect ratio through the algorithm. This parameter is used to judge whether the driver is fatigued, cooperate with the display screen and the light of the RGB small light to display the fatigue state, and issue a warning to the driver through the voice warning module, so that the driver can stay awake while driving and avoid traffic accidents. This design is implemented by an embedded system, which has the advantages of being mobile and deployable to the real driving environment and hardly disturbing the driver's driving. After the test of the simulated driving environment, the face detection and eye feature detection work normally, the anti-fatigue driving function has high accuracy in identifying the driver's fatigue state, and the system has high upgrade potential.

Citation Download Citation

Jing Yin, Jing Zhang, Xianlin Cao, Shunyu Yao, and Xueying Hu "Design of anti-fatigue driving system based on image recognition", Proc. SPIE 12328, Second International Conference on Optics and Image Processing (ICOIP 2022), 123281M (9 September 2022); https://doi.org/10.1117/12.2644261

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KEYWORDS

Eye

Image processing

Facial recognition systems

Detection and tracking algorithms

Cameras

Imaging systems

RGB color model

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