REFERENCES
- [1] John Wikander, Automated Vehicle Occupancy Technologies Study, USA: Texas Transportation Institute, Aug. (2007).
- [2] John Billheimer, Ken Kaylor and Charles Shade, Use of Videotape in HOV Lane Surveillance and Enforcement: Final Report, U.S. Department of Transportation, Sacramento, California, March (1990).
- [3] Shawn Turner, Video Enforcement of High Occupancy Vehicle Lanes: Field Test Results for I30 in Dallas, Transportation Research Record No. 1682, Transportation Research Board, Washington, D.C., pp. 2637 (1999).
- [4] Albert Gan, Rax Jung, Kaiyu Liu, Xin Li and Diego Sandoval, Vehicle Occupancy Data Collection Methods, Florida International University for Florida Department of Transportation, Feb. (2005).
- [5] Ioannis Pavlidis, Vassilios Morellas and Nikolaos Papanikolopoulos, “A Vehicle Occupant Counting System based on Near-Infrared Phenomenology and Fuzzy Neural Classification,” IEEE Transactions on Intelligent Transportation Systems, Vol. 1, pp. 7285 (2000).
- [6] Wood, J. W., Gimmestad, G. G. and Roberts, D. W., “Covert Camera for Screening of Vehicle Interiors and HOV Enforcement,” Proc. SPIE - The International Society for Optical Engineering, Vol. 5071, pp. 411 420 (2003).
- [7] John Canny, “A Computational Approach to Edge Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679698 (1986).
- [8] Kirsch, R., “Computer Determination of the Constituent Structure of Biological Images,” Computer Biomedical Research, Vol. 4, pp. 315328 (1971).
- [9] Moo, H., Chellappa, R. and Rosenfeld, A., “Performance Analysis of a Simple Vehicle Detection Algorithm,” Image and Vision Computing, Vol. 20, pp. 113 (2002).
- [10] Hao, X., Chen, H., Wang, C. and Yao, C., “Occupant Detection through Near-Infrared Imaging,” CrossStrait Conference on Infromation Science and Technology, Qinhuangdao,China, June, pp. 332335 (2010).
- [11] Rowley, H., Baluja, S. and Kanade, T., “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, pp. 2338 (1998).
- [12] Heisele, B., Serre, T. and Poggio, T., “A Component-Based Framework for Face Detection and Identification,” International Journal of Computer Vision, Vol. 74, pp. 167181 (2007).
- [13] Fleuret, F. and Geman, D., “Coarse-to-Fine Face Detection,” International Journal of Computer Vision, Vol. 41, pp. 85107 (2001).
- [14] Schneiderman, H. and Kanade, T., “Object Detection Using the Statistics of Parts,” International Journal of Computer Vision, Vol. 56, pp. 151177 (2004).
- [15] Viola, P. and Jones, M., “Rapid Object Detecting Using a Boosted Cascade of Simple Features,” Proc. CVPR, pp. 511518 (2001).
- [16] Yang, M.-H., “Face Detection,” Encyclopedia of Biometrics, Part 6, pp. 303308 (2009).
- [17] Lienhart, R., “An Extended Set of Haar-Like Features for Rapid Object Detection,” IEEE ICIP 2002, Vol. 1, pp. 900903 (2002).
- [18] Messom, C. H. and Barczak, A. L. C., “Fast and Efficient Rotated Haar-Like Features Using Rotated Integral Images,” Australian Conference on Robotics and Automation ACRA, pp. 16 (2006).
- [19] Abiantun, R. and Savvides, M., “Boosted Multi-Image Features for Improved Face Detection,” IEEE Applied Imagery Pattern Recognition (AIPR) Workshop, pp. 18 (2008).