嵌入式驾驶员状态检测算法的实现与优化

张旭; 李亚利; 陈晨; 王生进; 丁晓青

doi:10.3724/SP.J.1004.2012.02014

嵌入式驾驶员状态检测算法的实现与优化

doi: 10.3724/SP.J.1004.2012.02014

张旭^1,2,3,
李亚利^1,2,3,
陈晨⁴,
王生进^1,2,3,,
丁晓青^1,2,3

1.
智能技术与系统国家重点实验室北京 100084, 中国;
2.
清华信息科学与技术国家实验室北京 100084, 中国;
3.
清华大学电子工程系北京 100084, 中国;
4.
宾夕法尼亚大学计算机与信息科学系宾夕法尼亚州费城 PA 19104-6303, 美国

详细信息

通讯作者:
王生进

计量
- 文章访问数: 1785
- HTML全文浏览量: 64
- PDF下载量: 1284
- 被引次数: 0
出版历程
- 收稿日期: 2012-01-04
- 修回日期: 2012-03-20
- 刊出日期: 2012-12-20

Realization and Optimization of Embedded Driver Status Detection System

ZHANG Xu^1,2,3,
LI Ya-Li^1,2,3,
CHEN Chen⁴,
WANG Sheng-Jin^{1,2,3
,},
DING Xiao-Qing^1,2,3

1.
State Key Laboratory of Intelligent Technology and Systems, Beijing 100084, China;
2.
Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China;
3.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
4.
Department of Computer &Information Science, University of Pennsylvania, Philadelphia PA 19104-6303, USA

摘要

摘要: 提出了一种可以在嵌入式平台上实时运行的驾驶员状态检测算法. 状态检测采用了基于统计学习的Adaboost算法与动态建模算法. 与传统的采用主动红外光的方法相比, 本系统采用对人眼更为安全的被动式方法, 且对光线的变化有更好的鲁棒性. 算法的主要创新点是: 1) 提出了检测区域自适应调整的单双眼检测相结合的Adaboost人眼检测算法, 提高了人眼检测的准确性与速度; 2)提出基于高斯混合模型的人眼动态建模跟踪算法, 自动提取驾驶员眼睛区域灰度分布的信息, 实现了对不同驾驶员人眼的建模与跟踪定位. 在多个公共数据集以及实车采集的视频上进行的实验表明, 该算法能够准确判断驾驶员的状态, 满足实时处理的要求.
- 嵌入式系统 /
- 驾驶员状态检测 /
- 人眼动态模型 /
- 人眼跟踪
Abstract: This paper presents a novel driver status detection algorithm, which can be processed in embedded platform in real-time. The proposed method is based on Adaboost and dynamic modeling algorithm. Compared to the traditional active infrared radiation method, our system employs a safer passive way and the algorithm is more robust to the various illuminations. There are two main contributions: 1) it mixes up the single eye and eye pairs detectors together and presents an adaptive detection region Adaboost eye detection algorithm, improving the detection rate and speeding up the eye detection process; 2) it presents a dynamic eye modeling tracking algorithm which is based on the Gaussian mixture model. The tracking algorithm can automatically extract image intensity distribution of the driver's eye region, thus can model and track eyes of different drivers. Experiments on several public databases and driving sequences taken in real car show that the proposed method can detect the driver status precisely and satisfy the real time processing requirements.
- Embedded system /
- driver status detection /
- dynamic eye model /
- eye tracking

HTML全文

参考文献(1)

[1]

Li Li, Wang Fei-Yue, Zheng Nan-Ning, Zhang Yi. Research and developments of intelligent driving behavior analysis. Acta Automatica Sinica, 2007, 33(10): 1014-1022(李力, 王飞跃, 郑南宁, 张毅. 驾驶行为智能分析的研究与发展. 自动化学报, 2007, 33(10): 1014-1022)[2] Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137-154[3] Hansen D W, Ji Q. In the eye of the beholder: a survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(3): 478-500[4] Cootes T F, Taylor C J, Cooper D H, Graham J. Active shape models, their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59[5] Cootes T F, Edwards G, Taylor C J. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 681-685[6] Lee S J, Jo J, Jung H G, Park K R, Kim J. Real-time gaze estimator based on driver's head orientation for forward collision warning system. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 254-267[7] Ji Q, Zhu Z W, Lan P. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology, 2004, 53(4): 1052-1068[8] Lienhart R, Kuranov A, Pisarevsky V. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Pattern Recognition, 2003, 2781: 297-304[9] Wierwille W W, Wreggit S S, Kirn C L, Ellsworth L A, Fairbanks R J. Research on Vehicle Based Driver Status/performance Monitoring: Development, Validation, and Refinement of Algorithms for Detection of Driver Drowsiness. National Highway Traffic Safety Administration Final Report. DOT HS 808 247, Office of Crash Avoidance Research, USA, 1994[10] Zhang Chuang, Chi Jian-Nan, Zhang Zhao-Hui, Wang Zhi-Liang. The research on eye tracking for gaze tracking system. Acta Automatica Sinica, 2010, 36(8): 1051-1061(张闯, 迟健男, 张朝晖, 王志良. 视线追踪系统中眼睛跟踪方法研究. 自动化学报, 2010, 36(8): 1051-1061)[11] Horng W B, Chen C Y, Chang Y, Fan C H. Driver fatigue detection based on eye tracking and dynamic template matching. In: Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control. Taipei, China: IEEE, 2004. 7-12[12] Li Y L, Wang S J, Ding X Q. Eye/eyes tracking based on a unified deformable template and particle filtering. Pattern Recognition Letters, 2010, 31(11): 1377-1387[13] Gao W, Cao B, Shan S G, Chen X L, Zhou D L, Zhang X H, Zhao D B. The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Transaction on System Man, and Cybernetics (Part A), 2008,38(1): 149-161[14] Kanade T, Cohn J F, Tian Y L. Comprehensive database for facial expression analysis. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, France: IEEE, 2000. 46-53[15] Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-39[16] Zhang Z B, Chen Y Z, Yang Y Z. Driver fatigue detection system based on machine vision. In: Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE, 2008. 3979-3984

施引文献

资源附件(0)

访问统计