Traffic Video Based Cross Road Violation Detection and Peccant Vehicle Tracking

For the requirement of monitoring cross road violation in intelligent traffic system, a method to recognize and track the peccant vehicle is presented. The static background is modeled by mixture Gaussian model, and the location of lane line is detected by Hough transformation, thus, coordinated series can be obtained from the monitor image. Information of vehicles can be obtained by background-frame binary discrete wavelet transforms (BDWT) method, and according to the distance between the vehicle and line, the peccant vehicle can be detected. An improved mean-shift method is used to track the peccant vehicle, and a close range camera is used to snapshoot the license plate according to the center of tracking window. Actual road tests show that the work efficiency of this method is high, and the accuracy is up to 80%; run-time of mean-shift tracking system is about 0.085s for each frame. So it has a certain practical value in the field of intelligent traffic.

[1]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Pi Yi-ming.  Road Detection in SAR Image Based on Hough Transformation and Genetic Algorithm , 2005 .

[3]  Robert T. Collins,et al.  Mean-shift blob tracking through scale space , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Tao Gao,et al.  Moving video object segmentation based on redundant wavelet transform , 2008, 2008 International Conference on Information and Automation.

[8]  Xu Jianyuan.  The study of hough transformation real time detect algorithm , 2001 .

[9]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[10]  Zhang Jun.  BDWT based Moving Object Recognition and Mexico Wavelet Kernel Mean Shift Tracking , 2008 .

[11]  P. Wayne Power,et al.  Understanding Background Mixture Models for Foreground Segmentation , 2002 .

[12]  Dorin Comaniciu,et al.  Mean shift and optimal prediction for efficient object tracking , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).