The Mechanical Design of Drowsiness Detection Using Color Based Features
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
Peyman jabraelzade
1
()
Rahim parikhani
2
()
الکلمات المفتاحية: drowsiness, face detection, eye state detection, feature selection and PERCLOS,
ملخص المقالة :
This paper demonstrates design and fabrication o f a mechatronic system for human drowsiness detection. This system can be used in multiple places. For example, in factories, it is used on some dangerous machinery and in cars in order t o prevent the operator o r driver from falling asleep. This system is composed of three parts: (1) mechanical, (2) electrical and (3) image processing system. After processing the input image and eye position detection, the system investigates the state of the eye, and in the case of drowsiness, the system activates the alarm. It also has the ability to tra ck the eyes.
[1] G. Loy and A. Zelinsky. “Fast Radial Symmetry for Detecting Points of Interest”. IEEE Trans, On Pattern Analysis and Machine Intelligence, Vol. 25 No. 9, pp. 959-973, 2003.
[2] G. Pan, L. Sun., Z. Wu, S. Lao. “Eyeblink-based Antispoofing in Face Recognition from a Generic Webcamera”. The 11th IEEE International Conference on Computer Vision (ICCV'07), Rio de Janeiro, Brazil, October 14-20, 2007.
[3] K. Song, F. Shen, Z.L. Liu. "Eye detection and recognition in the fatigue warning system", in Proc of Third International Conference on Intelligent Networks and Intelligent Systems, ISBN. 978-0-7695-4249-2, 2010.
[4] L.Yunqi, Y. Meiling, S. Xiaobing, L. Xiuxia, O. Jiangfan. “Recognition of Eye States in Real Time Video”, IEEE Computer Society, 2009, pp. 554 – 559.
[5] L. Zhan-Feng, Z. Cuiqing, Z. Pei. “Driving Fatigue Detection Using MATLAB,” in IEEE, 2010.
[6] M. Padilla, Z. Fan. “EE368 Digital Image Processing Project Automatic Face Detection Using Color Based Segmentation and Template/Energy Thresholding”. Stanford University, Spring 2002-2003.
[7] T. P. Jung, S. Makeig, M. Stensmo, T.J. Sejnowski. “Estimating Alertness from the EEG Power Spectrum”, IEEE Transactions on Biomedical Engineering, vol 44, 1997, pp. 60-69.
[8] Y.S. Yeh, Z.W. Chou, C.W. Chen, K.N. Huang. "Study of the eye’s image processing for the determination of driver’s fatigue", in proceeding of 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1 – 4, 2009.
[9] Y.S. Wu, T.W. Lee, Q.Z. Wu, H.S. Liu. “An Eye State Recognition Method for Drowsiness Detection”, in proceeding of Vehicular Technology Conference, pp. 1 – 5, 2010.
[10] P. Viola, M. Jones and D. Snow. “Detecting Pedestrians Using Patterns of Motion and Appearance”. Presented at Proceedings of the ninth IEEE International Conference on Computer Vision (ICCV’03), 2003.
[11] P.Viola and M.Jones. “Robuast Real-time International Conference on Computer Vision”. Vol. 2, pp. 747, January 2001.
[12] T.Theocharides, N. Vijaykrishnan, and M. J. Irwin. “A parallel architecture for hardware face detection”,. Presented at Emerging VLSI Technologies and Architectures. IEEE Computer Society Annual Symposium on, 2006.
[13] Y.S. Huang, H.Y. Cheng, P.F. Cheng and C.Y. Tang. “Face Detection with High Precision Based on Radial-Symmetry Transform and EyePair Checking”. Proceedings of the IEEE International Conference, 2006. [14] Y.S. Wu, T.W. Lee, Q.Z. Wu, H.S. liu. "An Eye State Recognition Method for Drowsiness," in IEEE, 2010.
[15] Z. Zhang, J. Zhang. "A New Real-Time Eye Tracking for Driver Fatigue Detection". In Proceeding of 6th International Conference on ITS Telecommunications, pp. 8 – 11, 2006.
