Research on Driver's Fatigue Detection by Multi-feature Fusion
-
摘要: 针对驾驶疲劳检测中面部特征定位及驾驶员疲劳状态判别方法判断存在的不足,提出了利用监督下降算法同时定位驾驶员的多个面部特征。在眨眼、哈欠及点头判断的基础上,提取驾驶员眨眼频率、哈欠频率及点头频率多个特征值建立疲劳检测样本数据库,并构建朴素贝叶斯分类器进行疲劳判断。当驾驶员出现疲劳驾驶时及时给以警告信息,以预防交通事故发生。在实际的驾驶环境视频测试结果中,驾驶员疲劳状态的判别平均准确率达到了94.87%,具有较好的性能。Abstract: Aiming at the deficiencies of facial features location and driver fatigue judgments in driving fatigue detection, a new method called supervised descend method was proposed to locate driver's face features simultaneously. Driver's eye blink frequency, yaw frequency and nodding frequency are extracted to build the fatigue detection sample database based on eye blink, yawn and nodding judgments, then a naive Bayesian classifier was constructed to judge the driver's fatigue state. If a driver appears fatigue state during driving, warning message would be given promptly to prevent traffic accidents. In the actual driving environment video test result, the average accuracy rate of the driver's fatigue detection achieved 94.87%, with good performances.
-
[1] 肖赛,雷叶维.驾驶疲劳致因及监测研究进展[J].交通科技与经济,2017,19(4):14-19,63 Xiao S, Lei Y W. Research on the causes for driver fatigue and the monitoring technology progress[J]. Technology & Economy in Areas of Communications, 2017,19(4):14-19,63(in Chinese) [2] Satzoda R K, Trivedi M M. Drive analysis using vehicle dynamics and vision-based lane semantics[J]. IEEE Transactions on Intelligent Transportation Systems, 2015,16(1):9-18 [3] Zhang C, Wang H, Fu R R. Automated detection of driver fatigue based on entropy and complexity measures[J]. IEEE Transactions on Intelligent Transportation Systems, 2014,15(1):168-177 [4] 旷文腾,毛宽诚,黄家才,等.基于高斯眼白模型的疲劳驾驶检测[J].中国图象图形学报,2016,21(11):1515-1522 Kuang W T, Mao K C, Huang J C, et al. Fatigue driving detection based on sclera Gaussian model[J]. Journal of Image and Graphics, 2016,21(11):1515-1522(in Chinese) [5] 邓正宏,黄一杰,李翔,等.基于视频的驾驶疲劳检测技术的研究[J].西北工业大学学报,2015,33(6):1001-1006 Deng Z H, Huang Y J, Li X, et al. Researching driver fatigue detection using video technology[J]. Journal of Northwestern Polytechnical University, 2015,33(6):1001-1006(in Chinese) [6] Mbouna R O, Kong S G, Chun M G. Visual analysis of eye state and head pose for driver alertness monitoring[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(3):1462-1469 [7] Alioua N, Amine A, Rziza M. Driver's fatigue detection based on yawning extraction[J]. International Journal of Vehicular Technology, 2014,2014:678786 [8] Xiong X H, De La Torre F. Supervised descent method and its applications to face alignment[C]//Proceedings of 2013 IEEE Computer Vision and Pattern Recognition. Portland, OR, USA:IEEE, 2013:532-539 [9] Belhumeur P N, Jacobs D W, Kriegman D J, et al. Localizing parts of faces using a consensus of exemplars[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA:IEEE, 2011:545-552 [10] Viola P, Jones M J. Robust real-time object detection[J]. International Journal of Computer Vision, 2004,57(2):137-154 [11] Hartley L, Horberry T, Mabbott N, et al. Review of fatigue detection and prediction technologies[R]. Australia:National Road Transport Commission, 2000 [12] 吕健健.基于贝叶斯网络的驾驶员疲劳评估方法研究[D].辽宁大连:大连理工大学,2013 Lv J J. Study on driver fatigue alertness based on Bayesian network[D]. Liaoning Dalian:Dalian University of Technology, 2013(in Chinese) [13] Xiong X H. Supervised descent method[C]//Proceedings of the 2015 IEEE Computer Vision and Pattern Recognition. Boston, MA, USA:IEEE, 2015:2664-2673 [14] 李航.统计学习方法[M].北京:清华大学出版社,2012 Li H. Statistical learning methods[M]. Beijing:Tsinghua University Press,2012(in Chinese) [15] Kaplan S, Guvensan M A, Yavuz A G, et al. Driver behavior analysis for safe driving:a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2015,16(6):3017-3032
点击查看大图
计量
- 文章访问数: 234
- HTML全文浏览量: 44
- PDF下载量: 54
- 被引次数: 0