Vehicle Appearance Model for Recognition System Considering the Change of Imaging Condition
Keiji Kuwabara*, Yoshikazu Yano**, and Shigeru Okuma*
*Department of Electrical Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan
**Department of Electrical and Electronics Engineering, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota, Aichi 470-0392, Japan
We have proposed a technique to recognize a vehicle. In this technique, Gaussian Mixture Model (GMM) is adopted as a classifier. Vehicle appearance changed by imaging conditions such as time, weather and so on, and GMM parameters are also changed by imaging conditions. To recognize vehicle accurately, we have prepared some GMM tuned with the imaging conditions. On the other hand, it is impossible to prepare GMM because imaging condition changes successively. In this paper, we propose a method for estimating GMM and for training GMM parameters which reflect the successive change of imaging condition. Experimental results show that GMM parameters are estimated accurately and training of GMM are speeded up by proposed method.
-  G. Lefaix, E. Marchnd, and P. Bouthemy, “Motion-based Obstacle Detection and Tracking for Car Driving Assistance,” IAPR Int. Conf., Vol.4, pp. 74-77, 2002.
-  U. Handmann, T. Kalinke, C. Tzomakas, M. Werner, and W.v. Seelen, “An image processing system for driver assistance,” Image and Vision Computing, Vol.18, No.5, pp. 367-376, 2000.
-  R. Okade, Y. Taniguchi, K. Furukawa, and K. Onoguchi, “Obstacle detection using projective invariant and vanishing lines,” IEEE Int. Conf. Computer Vision, Vol.1, pp. 330-337, 2003.
-  T. Kato, Y. Ninomiya, and I. Masaki, “Preceding Vehicle Recognition Based on Learning From Sample Images,” IEEE Transactions on, Vol.3, No.4, pp. 252-260, 2002.
-  A. Gepperth, J. Edelbrunner, and T. Büucher, “Real-time detection and classification of cars in video sequences,” Proc. of the IEEE Symposium on Intelligent Vehicles, pp. 625-631, 2005.
-  Z. Sun, G. Bebis, and R. Miller, “Monocular Precrash Vehicle Detection : Features and Classifiers,” IEEE Transactions on Image Processing, Vol.15, No.7, pp. 2019-2034, 2006.
-  T. Okabe and Y. Sato, “Support Vector Machines for Object Recognition under Varying Illumination Conditions,” ACCV, 2004.
-  R. Gross and V. Brajovic, “An Image Preprocessing Algorithm for Illumination Invariant Face Recognition,” AVBPA, Springer, June 2003.
-  O. Nakayama, M. Shiohara, S. Sasaki, T. Takashima, and D. Ueno, “Robust Vehicle Detection under Poor Environmental Conditions for Rear and Side Surveillance,” IEICE Trans. Inf, Vol.E87-D, No.1, pp. 97-104, 2004.
-  R. Cucchiara and M. Piccardi, “Vehicle Detection under Day and Night Illumination,” ISCS-IIA99, pp. 789-794, 1999.
-  H. Maemoto, Y. Yano, and S. Okuma, “Parametric Vehicle Recognition Using Knowledge Acquisition System,” IEEE Int Conf, Vol.4, pp. 3982-3987, 2004.