Improved Fatigue Detection Using Eye State Recognition with HOG-LBP
- 2 Downloads
Fatigue driving is one of the main factors in traffic accidents. Many driver fatigue alert systems have been developed to prevent fatal car accidents. Existing fatigue detection methods using image processing usually fail in face of illumination and occlusion variations. In this paper, we propose a novel fatigue detection method using eye state recognition with HOG-LBP fused features. Our method first proposes HOG-LBP fusion schemes to combine the advantages of HOG and LBP features. Taking concatenated or additive fused feature as input, we design deep neural networks and train the model on CEW eye dataset for eye state recognition. We give an in-depth analysis to select the optimal fused coefficient for the best model. Based on eye state prediction results, we extract two eye-related features and design a decision criterion for fatigue detection. Experimental results prove the feasibility of the proposed method in detecting drowsiness level at different driving conditions.
KeywordsFatigue detection HOG-LBP fused feature Additive fusion Eye state recognition
This work was funded by a project that partially funded by National Science Foundation of China (51675265) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The authors gratefully acknowledge this support.
- 2.Wheaton, G., Shults, R.: Drowsy driving and risk behaviors 10 states and Puerto Rico. MMWR Morb. Mortal. Wkly Rep. 63(26), 557–562 (2014)Google Scholar
- 3.Qing, W., Bingxi, S., Bin, X. et al.: A perclos-based driver fatigue recognition application for smart vehicle space. In: Information Processing (ISIP), 2010 Third International Symposium on, pp. 437–441 (2010)Google Scholar
- 4.Friedrichs, F., Yang, B.: Drowsiness monitoring by steering and lane data based features under real driving conditions. In: European Signal Processing Conference, pp. 209–213 (2010)Google Scholar
- 5.Rogado, E., Garcia, L., Barea, R. et al.: Driver fatigue detection system. In: Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pp. 1105–1110 (2009)Google Scholar
- 8.Wierwille, W.W., Wregget, S., Kirn, C. et al.: Research on vehicle-based driver status/performance monitoring: Development validation and refinement of algorithms for detection of driver drowsiness. In: National Highway Traffice Safety Administration Final Report (1994)Google Scholar
- 9.Eriksson, M., Nikolaos, P.P.: Eye-tracking for detection of driver fatigue. In: IEEE Conference on Intelligent Transportation System, pp. 314–319 (1997)Google Scholar
- 10.Singh, S., Nikolaos, P.P.: Monitoring driver fatigue using facial analysis techniques. In: Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No. 99TH8383), pp. 314–318 (1999)Google Scholar
- 11.Smith, P., Shah, M., Lobo, N.D.: Monitoring head/eye motion for driver alertness with one camera. In: Proceedings 15th International Conference on Pattern Recognition (2000)Google Scholar
- 13.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)Google Scholar