The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks
Abstract
Driver's status is crucial because one of the main reasons for motor vehicular accidents is related to driver's inattention or drowsiness. Drowsiness detector on a car can reduce numerous accidents. Accidents occur because of a single moment of negligence, thus driver monitoring system which works in real-time is necessary. This detector should be deployable to an embedded device and perform at high accuracy. In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed. Main contribution of our paper is compression of heavy baseline model to a light weight model deployable to an embedded board. Moreover, minimized network structure was designed based on facial landmark input to recognize whether driver is drowsy or not. The proposed model achieved an accuracy of 89.5% on 3-class classification and speed of 14.9 frames per second (FPS) on Jetson TK1.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
FitNets: Hints for Thin Deep Nets
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.6550 · Cited by 2,859
Cited by
A Survey on State-of-the-Art Drowsiness Detection Techniques
Ramzan, M., Khan, H. U., Awan, S. M., Ismail, A., Ilyas, M., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2914373 · Cited by 361
Mazzia, V., Khaliq, A., Salvetti, F., & Chiaberge, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964608 · Cited by 212
Metadata
- Authors
- Reddy, B., Kim, Y.-H., Yun, S., Seo, C., & Jang, J.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 438-445
- OpenAlex
- W2738749209
- Cited by
- 260
- References
- 5
- FWCI
- 25.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Reddy, B., Kim, Y.-H., Yun, S., Seo, C., & Jang, J. (2017). Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2017.59
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