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Real-Time Computer Vision System for Continuous Face Detection and Tracking

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 18
Year of Publication: 2015
Authors:
Varsha E. Dahiphale
Sathyanarayana R
10.5120/21797-5100

Varsha E Dahiphale and Sathyanarayana R. Article: Real-Time Computer Vision System for Continuous Face Detection and Tracking. International Journal of Computer Applications 122(18):1-5, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Varsha E. Dahiphale and Sathyanarayana R},
	title = {Article: Real-Time Computer Vision System for Continuous Face Detection and Tracking},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {18},
	pages = {1-5},
	month = {July},
	note = {Full text available}
}

Abstract

The ever-increasing number of traffic accidents due to a diminished driver's vigilance level has become a problem of serious concern to society. With the ever growing traffic conditions, this problem will further deteriorate. For this issue, development of system which can actively monitors driver vigilance level and alert the driver for any insecure driving condition is essential. So this paper gives detailed information about driver vigilance level monitoring system. The ultimate goal of the system is to detect and alert the driver from insecure sleepy or low concentration driving condition. The system consists of two main modules including drivers face and eye detection module and drivers face tracking module. Viola Jones face detection with AdaBoost (Adaptive- Boosting) method and Circular Hough Transform technique are integrated in the drivers face and eye detection module. In the drivers face tracking module, CAMSHIFT (Continuously Adaptive Mean Shift) algorithm has been used for continuous face tracking of driver. The main components of the system consist of a video camera, a specially designed hardware system based on Raspberry Pi for real-time image processing and controlling the alarm system. In the proposed system, only one video camera is used in practice yet an achievement of fast and accurate detection results are obtained.

References

  • Mohamad-Hoseyn Sigari, Mahmood Fathy, and Mohsen Soryani Bowman, A Driver Face Monitoring System For Fatigue and Distraction Detection. Hindawi Publishing Corporation International Journal of Vehicular Technology Volume 2013.
  • Paul Viola and Michael J. Jones Robust Real-Time Face Detection In International Journal of Computer Vision 57(2), 137154, 2004.
  • Gary R. Bradski, Microcomputer Research Lab, Santa Clara, CA, Intel Corporation Computer Vision Face Tracking For Use in a Perceptual User Interface Intel Technology Journal Q2 98.
  • Yi-Qing Wang, An Analysis of the Viola-Jones Face Detection Algorithm, Image Processing On Line, 4 (2014), pp. 128–148.
  • G. Bradski, "The OpenCV Library", Dr. Dobb's Journal of Software Tools, 2000. Available: http://sourceforge. net/projects/opencvlibrary.
  • Matthew Sacco, Reuben A. Farrugia Driver Fatigue Monitoring System Using Support Vector Machines Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, ISCCSP 2012, Rome, Italy, 2-4 May 2012.
  • John Canny, A Computational Approach to Edge Detection", IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. Pam1- 8, NO. 6, November 1986.
  • Khary Popplewell, Kaushik Roy, Foysal Ahmad, and Joseph Shelton, Multispectral Iris Recognition Utilizing Hough Transform and Modified LBP, 2014 IEEE International Conference on Systems, Man, and Cybernetics October 5-8, 2014, San Diego, CA, USA.
  • Mohamad-Hoseyn Sigari, Muhammad-Reza Pourshahabi, Mohsen Soryani and Mahmood Fathy, "A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection," International Journal of Advanced Science and Technology Vol. 64 (2014).
  • Sung Joo Lee, Jaeik Jo, Ho Gi Jung, Kang Ryoung Park, and Jaihie Kim, "Real-Time Gaze Estimator Based on Drivers Head Orientation for Forward Collision Warning System," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 1, MARCH 2011.
  • Boon-Giin Lee and Wan-Young Chung, "Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals," IEEE SENSORS JOURNAL, VOL. 12, NO. 7, JULY 2012.
  • Dasgupta, Anjith George, S. L. Happy, and Aurobinda Routray, "A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 4, DECEMBER 2013.