Abstract
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.
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References
Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-Time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Power, P.W., Schoonees, J.A.: Understanding background mixture models for foreground segmentation. In: Proceedings of Image and Vision Computing, Auckland, New Zealand, pp. 267–271 (2002)
Jiang, Y.-h., Pi, Y.-m.: Road Detection in SAR Image Based on Hough Transformation and Genetic Algorithm. Radar Science and Technology 3(3), 156–162 (2005)
Wang, Q., Hu, W., Lu, Z., et al.: The Study of Hough Transformation Real Time Detect Algorithm. Computer Engineering and Design 22(3), 76–80 (2001)
Gao, T., Liu, Z.-g.: Moving Video Object Segmentation Based on Redundant Wavelet Transform. In: IEEE Int. Conf. on Information and Automation, Hunan, China, pp. 156–160 (2008)
Otsu, N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Trans. SMC 9(1), 62–66 (1979)
Bradski, G.: Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal 2(Q2) (1998)
Collins, R.: Mean-Shift Blob Tracking Through Scale Space. In: Proc. IEEE Conf. Comp., vol. 2, pp. 234–240 (2003)
Comaniciu, D., Ramesh, V.: Mean Shift and Optimal Prediction for Efficient Object Tracking. In: IEEE Inter-national Conference on Image Processing, Vancouver, Canada, vol. 3, pp. 70–73 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Gao, T., Liu, Z.-g., Zhang, J.: BDWT based Moving Object Recognition and Mexico Wavelet Kernel Mean Shift Tracking. Journal of System Simulation 20(19), 5236–5239 (2008)
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Gao, T., Liu, Zg., Zhang, J. (2009). Traffic Video Based Cross Road Violation Detection and Peccant Vehicle Tracking. In: Qi, L. (eds) Applied Computing, Computer Science, and Advanced Communication. FCC 2009. Communications in Computer and Information Science, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02342-2_14
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DOI: https://doi.org/10.1007/978-3-642-02342-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02341-5
Online ISBN: 978-3-642-02342-2
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