基于多形态红外特征与深度学习的实时驾驶员疲劳检测
[1] Katsis C D, Ntouvas N E, Bafas C G, et al. Assessment of muscle fatigue during driving using surface EMG[C]// Proceedings of the IASTED International Conference on Biomedical Engineering, 2004: 259-262.
[2] Ohsuga M, Kamakura Y, Roongroj Nopsuwanchai, et al. Classification of blink waveforms toward the assessment of driver′s arousal levels-an EOG approach and the correlation with physiological measures[C]//Engineering Psychology and Cognitive Ergonomics, 2007: 787-795.
[3] Takahashi I, Yokoyama K. Development of a feedback stimulation for drowsy driver using heartbeat rhythms[C]//Engineering in Medicine and Biology Society, 2011: 4153-4158.
[4] Jin Xue. Research on the detection method of fatigue driving based on driving behavior[D]. Beijing: Beijing University of Technology, 2015. (in Chinese)
[5] Celenk M, Eren H, Poyraz M. Prediction of driver head movement via Bayesian learning and ARMA modeling[C]//Intelligent Vehicles Symposium, 2009: 542-547.
[6] Friedrichs F, Yang B. Drowsiness monitoring by steering and lane data based features under real driving conditions[C]//European Signal Processing Conference, 2010: 209-213.
[7] Tao H, Zhao Y. Real-time driver fatigue detection based on face alignment[C]//International Conference on Digital Image Processing, 2017: 1042003.
[8] Meng C N, Bai J J, Zhang T N, et al. Eye movement analysis for activity recognition based on one web camera[J]. Acta Physica Sinica, 2013, 62(17): 116-121.
[9] Cheng Ruzhong, Zhao Yong, Dai Yong, et al. An on-board embedded driver fatigue warning system based on adaboost method[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2012, 48(5): 719-726. (in Chinese)
[10] Kuang Wenteng, Mao Kuancheng, Hong Jiacai, et al. Fatigue driving detection based on sclera Gaussian model[J]. Journal of Image and Graphics, 2016, 21(11): 1515-1522.
[12] Boser B, Denker J S. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551.
[13] Farhan M, Yli-Harja O, Niemist A. A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search [J]. Pattern Recognition, 2013, 46(3):741-751.
[14] Alex Krizhevsky, Llya Sutskever, Geoffrey E, et al. ImageNet classification with deep convolutional neural networks[J]. Communications of the Acm, 2012, 60(2): 2012.
[15] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Computer Vision and Pattern Recognition, 2014: 580-587.
[16] Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision, 2015: 1440-1448.
[17] Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]// International Conference on Neural Information Processing Systems, 2015: 91-99.
[18] Redmon J, Divvala S, Girshick R, et al. You only look once: unified real-time object detection [C]//Computer Vision and Pattern Recognition, 2015, arxiv: 1506, 02640.
[19] Donahue J, Hendricks L A, Rohrbach M, et al. Long-term recurrent convolutional networks for visual recognition and description[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4): 677-691.
[20] Zhang K, Zhang Z, Li Z, et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10): 1499-1503.
[21] Li Rui. Facial expression recognition method based on feature fusion[D]. Hefei: Hefei University of Technology, 2013. (in Chinese)
[22] Hu Feng. Multi-source information fusion application to driving fatigue detection[D]. Hefei: Anhui University of Science and Technology, 2014. (in Chinese)
[23] Chen Mingchu. Research on driver fatigue detection based on eye state[D]. Chongqing: Chongqing University, 2012.(in Chinese)
[24] Ma Ying. Research on driver fatigue alarm system based on facial features[D]. Hefei: Anhui University of Technology,2016. (in Chinese)
[25] Zhang F, Su J, Geng L, et al. Driver fatigue detection based on eye state recognition[C]//International Conference on Machine Vision and Information Technology, 2017: 105-110.
[26] Sepp Hochreiter, Jürgen Schmidhuber. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[27] Ma X, Hovy E. End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF[C]//Meeting of the Association for Computational Linguistics, 2016: 1064-1074.
[28] He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on imageNet classification[C]//IEEE International Conference on Computer Vision, 2016: 1026-1034.
耿磊, 梁晓昱, 肖志涛, 李月龙. 基于多形态红外特征与深度学习的实时驾驶员疲劳检测[J]. 红外与激光工程, 2018, 47(2): 0203009. Geng Lei, Liang Xiaoyu, Xiao Zhitao, Li Yuelong. Real-time driver fatigue detection based on morphology infrared features and deep learning[J]. Infrared and Laser Engineering, 2018, 47(2): 0203009.