Real-time Fatigue Driving Recognition System Based on Deep Learning and Embedded Platform

Lei Geng, ZhiQiang Hu, ZhiTao Xiao


The frequent occurrence of automobile traffic accidents seriously threatens the safety of human life and property. Therefore, fatigue driving detection has important social value and research significance. In consideration of the market demand of intelligent assistant driving system, we design a real-time driver fatigue detection system based on deep learning and ARM platform, which uses Samsung 6818A53 series ARM as the driver fatigue real-time detection platform. In order to reduce the interference caused by the change of light and the occlusion of sunglasses in the actual driving environment, the driver's face image is captured by USB infrared camera. Firstly, face detection and alignment are carried out by multi-task cascaded convolutional neural network; Then the eye region is obtained according to geometric relationship between the feature points; Moreover, the driver's eye state is identified by Convolutional Neural Network (CNN); Finally, fatigue judgment is made based on PERCLOS criterion. The system has been tested in the experimental simulation environment and the actual driving environment. The experimental results show that detection speed of the system can reach more than 20 frames per second, which meets the requirement of real-time detection.


Fatigue Driving; Embedded Platform; CNN; PERCLOS; Real-time Detection.

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Pratama B G, Ardiyanto I, Adji T B. A review on driver drowsiness based on image, bio-signal, and driver behavior[C]//2017 3rd International Conference on Science and Technology-Computer (ICST). IEEE, 2017: 70-75.

Triyanti V, Iridiastadi H. Challenges in detecting drowsiness based on driver’s behavior[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2017, 277(1): 012042.

Mardi Z, Ashtiani S N M, Mikaili M. EEG-based drowsiness detection for safe driving using chaotic features and statistical tests[J]. Journal of medical signals and sensors, 2011, 1(2): 130.

Jung S J, Shin H S, Chung W Y. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel[J]. IET Intelligent Transport Systems, 2014, 8(1): 43-50.

Fatourechi M, Bashashati A, Ward R K, et al. EMG and EOG artifacts in brain computer interface systems: A survey[J]. Clinical neurophysiology, 2007, 118(3): 480-494.

Gasser T, Sroka L, Möcks J. The transfer of EOG activity into the EEG for eyes open and closed[J]. Electroencephalography and clinical neurophysiology, 1985, 61(2): 181-193.

Li Z, Li S, Li R, et al. Online detection of driver fatigue using steering wheel angles for real driving conditions[J]. Sensors, 2017, 17(3): 495.

Ma J, Murphey Y L, Zhao H. Real time drowsiness detection based on lateral distance using wavelet transform and neural network[C]//2015 IEEE symposium series on computational intelligence. IEEE, 2015: 411-418.

Alioua N, Amine A, Rziza M. Driver’s fatigue detection based on yawning extraction[J]. International journal of vehicular technology, 2014, 2014.

Choi I H, Kim Y G. Head pose and gaze direction tracking for detecting a drowsy driver[C]//2014 international conference on big data and smart computing (BIGCOMP). IEEE, 2014: 241-244.

Karchani M, Mazloumi A, Saraji G N, et al. Presenting a model for dynamic facial expression changes in detecting drivers’ drowsiness[J]. Electronic physician, 2015, 7(2): 1073.

Dinges D F, Grace R. PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance[J]. US Department of Transportation, Federal Highway Administration, Publication Number FHWA-MCRT-98-006, 1998.

LeCun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436.

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.

Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.

Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.

Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.

Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.

Oquab M, Bottou L, Laptev I, et al. Learning and transferring mid-level image representations using convolutional neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 1717-1724.

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.


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