1. bookTom 27 (2022): Zeszyt 1 (June 2022)
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eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
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Angielski
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Detection of Driver Dynamics with VGG16 Model

Data publikacji: 23 Aug 2022
Tom & Zeszyt: Tom 27 (2022) - Zeszyt 1 (June 2022)
Zakres stron: 83 - 88
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski

[1] O. Ursulescu, B. Ilie, and G. Simion, “Driver drowsiness detection based on eye analysis”, in 2018 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2018, pp. 1–4. https://doi.org/10.1109/ISETC.2018.8583852 Search in Google Scholar

[2] R. O. Mbouna, S. G. Kong, and M. G. Chun, “Visual analysis of eye state and head pose for driver alertness monitoring”, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1462–1469, Sep. 2013. https://doi.org/10.1109/TITS.2013.2262098 Search in Google Scholar

[3] S. Chinara,”Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal”, Journal of Neuroscience Methods, vol. 347, 2021, Art no. 108927. https://doi.org/10.1016/j.jneumeth.2020.10892732941920 Search in Google Scholar

[4] M. Dua, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection”, Neural Computing and Applications, vol. 33, no. 8, pp. 3155–3168, Jul. 2021. https://doi.org/10.1007/s00521-020-05209-7 Search in Google Scholar

[5] K. Dwivedi, K. Biswaranjan, and A. Sethi, “Drowsy driver detection using representation learning”, in Advance Computing Conference (IACC), Gurgaon, India, Mar. 2014, pp. 995–999. https://doi.org/10.1109/IAdCC.2014.6779459 Search in Google Scholar

[6] S. Park, F. Pan, S. Kang, and C. D. Yoo, “Driver drowsiness detection system based on feature representation learning using various deep networks”, in Asian Conference on Computer Vision, Taipei, Taiwan, Nov. 2016, pp. 154–164. https://doi.org/10.1007/978-3-319-54526-4_12 Search in Google Scholar

[7] S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD: A yawning detection dataset”, in Proceedings of the 5th ACM Multimedia Systems Conference, Singapore, Mar. 2014, pp. 24–28. https://doi.org/10.1145/2557642.2563678 Search in Google Scholar

[8] D. Cireşan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification”, arXiv: 1202.2745, Tech Rep. No. IDSIA-04-12, Feb. 2012. [Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/1202.2745.pdf Search in Google Scholar

[9] B. Boser, J. D. Y. Le Cun, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network”, Advances in Neural Information Processing, vol. 2, 1989. Search in Google Scholar

[10] K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv, preprint arXiv:1409.1556, 2014. Search in Google Scholar

[11] J. Gwak, A. Hirao, and M. Shino, “An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing”, Appl. Sci., vol. 10, no. 8, Apr. 2020, Art no. 2890. https://doi.org/10.3390/app10082890 Search in Google Scholar

[12] S. Mehta, S. Dadhich, S. Gumber, and A. J. Bhatt, “Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio”, in Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Jaipur, India, Feb. 2019. https://doi.org/10.2139/ssrn.3356401 Search in Google Scholar

[13] Z. Kepesiova, J. Ciganek, and S. Kozak, “Driver drowsiness detection using convolutional neural networks”, in 2020 Cybernetics & Informatics (K&I), Velke Karlovice, Czech Republic, Mar. 2020, pp. 1–6. https://doi.org/10.1109/KI48306.2020.9039851 Search in Google Scholar

[14] R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, “Driver drowsiness detection model using convolutional neural networks techniques for android application”, in Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, Doha, Qatar, May 2020, pp. 2–5. https://doi.org/10.1109/ICIoT48696.2020.9089484 Search in Google Scholar

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