參考文獻 |
[1] Drowsy Driving NHTSA reports, Jun. 02, 2017. Retrieved from https://www.nhtsa.gov/risky-driving/drowsy-driving.
[2] Distracted Driving NHTSA reports, Sep. 08, 2016. Retrieved from https://www.nhtsa.gov/risky-driving/distracted-driving.
[3] H. Malik, F. Naeem, Z. Zuberi, and R. ul Haq, “Vision based driving simulation,” in 2004 International Conference on Cyberworlds, pp. 255–259, Nov. 2004.
[4] Z. Mardi, S. N. Ashtiani, and M. Mikaili, “EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests,” J. Med. Signals Sens., vol. 1, no. 2, pp. 130–137, 2011.
[5] Y. Abouelnaga, H. M. Eraqi, and M. N. Moustafa, “Real-time Distracted Driver Posture Classification,” arXiv:1706.09498, Nov. 2018.
[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 Computer Vision – ACCV 2016 Workshops, pp. 154–164, 2017.
[7] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
[8] J. Yu, S. Park, S. Lee, and M. Jeon, “Representation Learning, Scene Understanding, and Feature Fusion for Drowsiness Detection,” in Computer Vision – ACCV 2016 Workshops, Cham, pp. 165–177, 2017.
[9] J. Lyu, Z. Yuan, and D. Chen, “Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection,” arXiv:1801.02325, Jan. 2018.
[10] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
[11] W. Liu, J. Qian, Z. Yao, X. Jiao, and J. Pan, “Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection,” Future Internet, vol. 11, no. 5, Art. no. 5, May 2019.
[12] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1499–1503, Oct. 2016.
[13] S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, Oct. 2010.
[14] L. Wang et al., “Temporal Segment Networks: Towards Good Practices for Deep Action Recognition,” arXiv:1608.00859, Aug. 2016.
[15] M. Zolfaghari, K. Singh, and T. Brox, “ECO: Efficient Convolutional Network for Online Video Understanding,” arXiv:1804.09066, May 2018.
[16] A. G. Howard, “Some Improvements on Deep Convolutional Neural Network Based Image Classification,” arXiv:1312.5402, Dec. 2013.
[17] C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, p. 60, Jul. 2019.
[18] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., pp. 1097–1105, 2012.
[19] M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” arXiv:1311.2901, Nov. 2013.
[20] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556, Apr. 2015.
[21] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv:1512.03385, Dec. 2015.
[22] C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1–9, Jun. 2015.
[23] S. Ji, W. Xu, M. Yang, and K. Yu, “3D Convolutional Neural Networks for Human Action Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221–231, Jan. 2013.
[24] D. Tran, J. Ray, Z. Shou, S.-F. Chang, and M. Paluri, “ConvNet Architecture Search for Spatiotemporal Feature Learning,” arXiv:1708.05038, Aug. 2017.
[25] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning Spatiotemporal Features with 3D Convolutional Networks,” in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 4489–4497, Dec. 2015.
[26] M. Lin, Q. Chen, and S. Yan, “Network In Network,” arXiv:1312.4400, Mar. 2014.
[27] S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv:1502.03167, Mar. 2015.
[28] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, US, pp. 2818–2826, Jun. 2016.
[29] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” arXiv:1602.07261, Aug. 2016.
[30] S. S. Farfade, M. J. Saberian, and L.-J. Li, “Multi-view Face Detection Using Deep Convolutional Neural Networks,” in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR ’15, Shanghai, China, pp. 643–650, 2015.
[31] S. Bambach, S. Lee, D. J. Crandall, and C. Yu, “Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions,” Proc IEEE Int Conf Comput Vis, vol. 2015, pp. 1949–1957, Dec. 2015.
[32] G. Farnebäck, “Two-Frame Motion Estimation Based on Polynomial Expansion,” in Image Analysis, vol. 2749, J. Bigun and T. Gustavsson, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 363–370, 2003.
[33] W. Kay et al., “The Kinetics Human Action Video Dataset,” arXiv:1705.06950, May 2017.
[34] J. Carreira and A. Zisserman, “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 4724–4733, Jul. 2017.
[35] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv:1207.0580, Jul. 2012.
[36] C.-H. Weng, Y.-H. Lai, and S.-H. Lai, “Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network,” in Computer Vision – ACCV 2016 Workshops, Cham, pp. 117–133, 2017.
[37] S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD: A yawning detection dataset,” pp. 24–28, Mar. 2014.
[38] H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “HMDB: A large video database for human motion recognition,” in 2011 International Conference on Computer Vision, pp. 2556–2563, Nov. 2011.
[39] A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: Fast and Flexible Image Augmentations,” Information, vol. 11, no. 2, Art. no. 2, Feb. 2020. |