A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network-Reference-Cited by-同舟云学术

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A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network

Published:2023-06-28 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Cheng Wangfeng,Wang Xuanyao^ORCID,Mao Bangguo

Funder

Anhui Provincial Natural Science Foundation under Grant

Scientific Research Activities of Post-Doctoral Researchers in Anhui Province

Environmentally Friendly Materials and Occupational Health Research Institute, Anhui University of Science and Technology

Anhui University of Science and Technology Postgraduate Innovation Fund Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-02927-6.pdf

Reference38 articles.

1. Chen Y, Xiang Z, Du, W. Improving lane detection with adaptive homography prediction. Vis Comput (2022).

2. Li H T, Todd Z, Bielski N, et al. 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Vis Comput. 1–16 (2021)

3. Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19(11), 2574 (2019)

4. Luo R C, Hsu C H, Wen Y C. Multi-model fusion on real-time drowsiness detection for telemetric robotics tracking applications. In: 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS). IEEE, 2020; pp. 1–6.

5. Ma, D., Luo, X., Jin, S., et al.: Lane-based saturation degree estimation for signalized intersections using travel time data. IEEE Intell Transp Syst Mag 9(3), 136–148 (2017)

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