Driver Fatigue Monitoring Based on Facial Multifeature Fusion
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8761-0_9
Reference39 articles.
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3. Arefnezhad, S., Samiee, S., Eichberger, A., Frühwirth, M., Kaufmann, C., Klotz, E.: Applying deep neural networks for multi-level classification of driver drowsiness using vehicle-based measures. Expert Syst. Appl. 162, 113778 (2020)
4. Li, K., Gong, Y., Ren, Z.: A fatigue driving detection algorithm based on facial multi-feature fusion. IEEE Access 8, 101244–101259 (2020)
5. Pilataxi, J., Vinan, W., Chavez, D.: Design and implementation of a driving assistance system in a car-like robot when fatigue in the user is detected. IEEE Latin Am. Trans. 14(2), 457–462 (2016)
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