Research on Driving Fatigue Monitoring Based on Tree-Structure Classifier

Liang Xiu Zhang; Xu Yun Qiu; Rong Chun Guo; Zhu Lin Zhang

doi:10.4028/www.scientific.net/AMR.490-495.1506

Paper Titles

Transformer Winding Deformation Fault Diagnose Based on FRA and Energy Feature Extraction by Wavelet Packet
p.1486

Study on EGR Control Strategy for Vehicle Diesel Engine Based on Experiment
p.1491

Robust Predictive Control for Uncertain Time-Delay Systems with Nonlinear Disturbance
p.1496

The Numerical Simulation of Flow Field of Target Type Impinging Stream Nozzle Based on Fluent
p.1501

Research on Driving Fatigue Monitoring Based on Tree-Structure Classifier
p.1506

The Improvement of Pseudo-Boolean Satisfiability Algorithm for FPGA Routing
p.1511

Thermal Error Modeling and Compensation of a CNC Machining Center
p.1516

The Temperature Measurement Method Based on the Image of Mechanical Part during Heating Treatment
p.1521

Study on Fractal Box Dimension and Its Application to Fault Diagnosis of Hydraulic Pump
p.1526

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495Research on Driving Fatigue Monitoring Based on...

Research on Driving Fatigue Monitoring Based on Tree-Structure Classifier

Abstract:

The driving fatigue monitoring system based on tree structure classifier is presented. The realtime fatigue monitoring system is established by training face and eye tree-structure classifiers which consists of many strong classifiers. Each strong classifier choose several weak classifiers by AdoBoost recursive method and each weak classifier is characterized by single Haar-like features. The whole Tree-structure is constructed by clustering branches recursive algorithm. Experiment has shown that the driving fatigue monitoring system based on tree-structure classifiers is very reliable. Introduction

You might also be interested in these eBooks

Mechatronics and Intelligent Materials II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

1506-1510

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.1506

Citation:

Cite this paper

Online since:

March 2012

Authors:

Liang Xiu Zhang, Xu Yun Qiu, Rong Chun Guo, Zhu Lin Zhang

Keywords:

Driving Fatigue Monitoring, Eye Detection, Face Detection, Tree Classifier

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] W. Tiesheng, The Video Analysis Method for Driver Drowsiness Detection, D. Shanghai Jiaotong Universirty. (2005).

Google Scholar

[2] Z. Zhiwei, Robust real-time eye detection and tracking under variable lighting conditions and various face orientations, J. Computer Vision and Image Understanding. (2005)124-154.

DOI: 10.1016/j.cviu.2004.07.012

Google Scholar

[3] P. Viola, M. Jones, Rapid Object Detection using a Boosted Cascade of Simple Features, C. IEEE Conference on Computer Vision and Pattern Recognition. (2001)511-518.

DOI: 10.1109/cvpr.2001.990517

Google Scholar

[4] R. Lienhat, A. Kuranov, V. Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection, C. Proceedings of the 25th German Pattern Recognition Symposium(2003)297-304.

DOI: 10.1007/978-3-540-45243-0_39

Google Scholar

[5] J. Friedman, T. Hastie, R. Tibshirani, Additive logisticregression: a statistical view of boosting, J. The Annals of Statistics(2000)337-407.

Google Scholar

[6] Z. liangxiu. Realtime On-Road Vehicle Detection Approach Based on Tree Structure, J. JOURNAL OF QINGDAO UNIVERSITY ( E&T), (2008)49-53.

Google Scholar