Fatigue Detection System Based on Kalman Filtering and Dynamic Template

Lan Shi; Yi Hong  Zhang

doi:10.4028/www.scientific.net/AMM.380-384.3921

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Fatigue Detection System Based on Kalman Filtering...

Fatigue Detection System Based on Kalman Filtering and Dynamic Template

Abstract:

In order to reduce the incidence of traffic accidents, it did detailed analysis and further research on fatigue detection system.this system include face detection, eye detection, eye tracking. During the eye detection, it proposed a new approach based on Kalman filtering and dynamic template. And then it did experiments on the detection rate, the PERCLOSs numerical value and the speed. Experiment results show that the detection results can meet the demand of practice. It turns out that this system can meet the demand of basic practice, has an extensive application field.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

3921-3924

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.3921

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Online since:

August 2013

Authors:

Lan Shi, Yi Hong Zhang

Keywords:

Dynamic Template, Eye Detection, Kalman Filtering

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References

[1] A. Vuckovic, D. Popovic, V. Radivojevic. Artificial neural network for detecting drowsiness from EEG recordings[C]. Neural Network Applications in Electrical Engineering, 2002, Page(s): 155-158.

DOI: 10.1109/neurel.2002.1057990

Google Scholar

[2] PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance. http: /www. fmcsa. dot. gov/documents/tb98-006. pdf.

Google Scholar

[3] R. L. Hsu, M. Abdel-Mottaleb, A. K. Jain. Face Detection in Colour Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24 No. 5, 696–706, (2002).

DOI: 10.1109/34.1000242

Google Scholar

[4] Pierre D. Wellner. Adaptive Thresholding for the DigitalDesk. Technical Report EPC-1993-110, EuroPARC.

Google Scholar

[5] Derek Bradley, Gerhard Roth. Adaptive Thresholding Using the Integral Image. Journal of graphics tools, Vol. 12 No. 2, 13-21, (2007).

DOI: 10.1080/2151237x.2007.10129236

Google Scholar

[6] Lee J Y and Yoo S I. An elliptical boundary model for skin color detection [C]. In Proc. of the International Conference on Imaging Science, Systems, and Technology, Las Vegas, USA, 2002: 579-584.

Google Scholar

[7] W.R. Song, Y. Zhang . Kalman Filtering. Science Press, Beijing, China, (1991).

Google Scholar