Fatigue driving detection based on Haar feature and extreme learning machine

Abstract As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers' fatigue driving by using the human eye tracking technology.

[1]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[2]  Yiqiang Chen,et al.  Constraint Online Sequential Extreme Learning Machine for lifelong indoor localization system , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[3]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[4]  Zheng Chang,et al.  Recognition of Human Head Movement Trajectory Based on Three-Dimensional Somatosensory Technology , 2014 .

[5]  Janusz Konrad,et al.  Action Recognition Using Sparse Representation on Covariance Manifolds of Optical Flow , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[6]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[9]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[10]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[11]  Rajiv Soundararajan,et al.  Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Damon M. Chandler,et al.  A spatiotemporal most-apparent-distortion model for video quality assessment , 2011, 2011 18th IEEE International Conference on Image Processing.

[13]  Ren Fei An Automatic Recognition Approach of Human Gestures , 2010 .

[14]  Zheng Chang,et al.  Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition , 2015 .

[15]  Zhu Zhen-zhe Real-time Detection of Facial Eye Status Based on Kinect Sensor , 2015 .

[16]  Margaret H. Pinson,et al.  A new standardized method for objectively measuring video quality , 2004, IEEE Transactions on Broadcasting.