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SUN ET AL.: ON-ROAD VEHICLE DETECTION: A REVIEW

[99] R. Collins and Y. Liu, “On-Line Selection of Discriminant Tracking Features,” Proc. IEEE Int’l Conf. Computer Vision, 2003. [100] J. Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004. [101] J. Collado, C. Hilario, A. de la Escalera, and J. Armingol, “Model- Based Vehicle Detection for Intelligent Vehicles,” Proc. IEEE Intelligent Vehicles Symp., 2004. [102] K. She, G. Bebis, H. Gu, and R. Miller, “Vehicle Tracking Using On-Line Fusion of Color and Shape Features,” Proc. IEEE Int’l Conf. Intelligent Transportation Systems, 2004. [103] J. Wang, G. Bebis, and R. Miller, “Overtaking Vehicle Detection Using Dynamic and Quasi-Static Background Modeling,” Proc. IEEE Workshop Machine Vision for Intelligent Vehicles, 2005. [104] Z. Sun, G. Bebis, and R. Miller, “Improving the Performance of On-Road Vehicle Detection by Combining Gabor and Wavelet Features,” Proc. IEEE Fifth Int’l Conf. Intelligent Transportation Systems, Sept. 2002. [105] L. andreone, P. Antonello, M. Bertozzi, A. Broggi, A. Fascioli, and D. Ranzato, “Vehicle Detection and Localization in Infra-Red Images,” Proc. IEEE Fifth Int’l Conf. Intelligent Transportation Systems, 2002. [106] P. Barham, X. Zhang, L. Andreone, and M. Vache, “The Development of a Driver Vision Support System Using Far Infrared Technology: Progress to Date on the Darwin Project,” Proc. IEEE Intelligent Vehicles Symp., 2000. [107] D. Pomerleau and T. Jochem, “Automatic Vehicle Detection in Infrared Imagery Using a Fuzzy Inference-Based Classification System,” IEEE Trans. Fuzzy Systems, vol. 9, no. 1, pp. 53-61, 2001. [108] M. Kagesawa, S. Ueno, K. Ikeuchi, H. Kashiwagi, “Recognizing Vehicles in Infrared Images Using IMAP Parallel Vision Board,” IEEE Trans. Intelligent Transportation Systems, vol. 2, no. 1, pp. 10- 17, 2001. [109] K. Sobottka, E. Meier, F. Ade, and H. Bunke, “Towards Smarter Cars,” Proc. Int’l Workshop Sensor Based Intelligent Robots, pp. 120- 139, 1999. [110] T. Spirig, M. Marle, and P. Seitz, “The MultiTap Lock-In CCD with Offset Subtraction,” IEEE Trans. Electron Devices, pp. 1643-1647, 1997. [111] K. Yamada, “A Compact Integrated Vision Motion Sensor for Its Applications,” IEEE Trans. Intelligent Transportation System, vol. 4, no. 1, pp. 35-41, 2003. [112] C. Stiller, J. Hipp, C. Rossig, and A. Ewald, “Multisensor Obstacle Detection and Tracking,” Image and Vision Computing, vol. 18, pp. 389-396, 2000. [113] Y. Fang, M. Mizuki, I. Masaki, and B. Horn, “TV Camera-Based Vehicle Motion Detection and Its Chip Implementation,” Proc. IEEE Intelligent Vehicle Symp., pp. 134-139, 2000. [114] W. Enkelmann, V. Gengenbach, and W. Kruger, “Obstacle Detection by Real-Time Optical Flow Evaluation,” Proc. IEEE Intelligent Vehicle Symp., pp. 97-102, 1994. [115] S. Ghiasi, H. Moon, and M. Sarrafzadeh, “Collaborative and Reconfigurable Object Tracking,” Proc. Int’l Conf. Eng. of Reconfi- gurable Systems and Algorithms, pp. 13-20, 2003. [116] A. Benedetti and P. Perona, “Real-Time 2-D Feature Detection on a Reconfigurable Computer,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1998. [117] R. Mandelbaum, M. Hansen, P. Burt, and S. Baten, “Vison for Autonomous Mobilty: Image Processing on the Vfe-200,” Proc. IEEE Int’l Symp. Intelligent Control, 1998. [118] J. Phillips, H. Moon, S. Rizvi, and J. Rauss, “The Feret Evaluation Methodology for Face-Recognition Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, pp. 1090-1104, 2000. [119] “Real-Time Event Detection Solutions (CREDS) for Enhanced Security and Safety in Public Transportation Challenge,” Proc. IEEE Int’l Conf. Advanced Video and Signal Based Surveillance, 2005. [120] M. Boshra and B. Bhanu, “Predicting Object Recognition Performance under Data Uncertainty, Occlusion and Clutter,” Proc. IEEE Int’l Conf. Image Processing, 1998. [121] M. Nishigaki, M. Saka, T. Aoki, H. Yuhara, and M. Kawai, “Fail Ouput Algorithm of Vision Sensing,” Proc. IEEE Intelligent Vehicle Symp., pp. 581-584, 2000.

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Zehang Sun received the BEng degree in telecommunication and the MEng degree in digital signal processing from Northern Jiaotong University, China, in 1994 and 1997, respec- tively, the MEng degree in electrical and electronic engineering from Nanyang Technolo- gical University, Singapore, in 1999, and the PhD degree in computer science and engineer- ing from the University of Nevada, Reno, in 2003. Dr. Sun joined eTreppid Technologies, LLC immediately upon his graduation. His expertise is in the area of real- time computer vision systems, statistical pattern recognition, artificial intelligence, digital signal processing, and embedded systems. He is a member of the IEEE.

George Bebis received the BS degree in mathematics and the MS degree in computer science from the University of Crete, Greece, in 1987 and 1991, respectively, and the PhD degree in electrical and computer engineer- ing from the University of Central Florida, Orlando, in 1996. Currently, he is an associate professor in the Department of Computer Science and Engineering at the University of Nevada, Reno (UNR) and the director of the UNR Computer Vision Laboratory (CVL). His research interests include computer vision, image processing, pattern recognition, machine learning, and evolutionary computing. His research is currently funded by US NSF, NASA, ONR, and the Ford Motor Company. Dr. Bebis is an associate editor of the Machine Vision and Applications Journal and serves on the editorial board of the Pattern Recognition Journal and the International Journal Artificial Intelligence Tools. He has served on the program committees of various national and international conferences, and has organized and chaired several conference sessions. In 2002, he received the Lemelson Award for innovation and Entrepreneurship. He is a member of the IEEE and the IAPR Educational Committee.

Ronald Miller received the BS degree in physics in 1983 from the University of Massachusetts, and the PhD degree in physics from the Massa- chusetts Institute of Technology in 1988. His research has ranged from computational model- ing of plasma and ionospheric instabilities to automotive safety applications. Dr. Miller heads a research program at Ford Motor Company in intelligent vehicle technologies focusing on ad- vanced RF communication, radar, and optical sensing systems for accident avoidance and telematics.

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