Abstract
For particle filtering tracking method, particle choosing is random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method is presented. A moving vehicle is detected by redundant discrete wavelet transforms method (RDWT), and then the key points are obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method are used as the initial particles to improve the tracking performance. Experimental results show that more particles centralize in the region of motion area by the presented method than traditional particle filtering, and tracking results of moving vehicles are more accurate. The method has been adopted by Tianjin traffic bureau of China, and has a certain actual application prospect.
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References
Zhang, L., Han, J., He, W., Tang, R.S.: Matching Method Based on Self-adjusting Template Using in Tracking System. Journal of Chongqing University (Natural Science Edition) (06), 74–76 (2005)
Magee, D.R.: Tracking Multiple Vehicles Using Foreground, Background and Motion Models. Image and Vision Computing 22, 143–155 (2004)
Liu, H., Jiang, G., Li, W.: A Multiple Objects Tracking Algorithm Based on Snake Model. Computer Engineering and Applications 42(7), 76–79 (2006)
Comaniciu, D., Ramesh, V.: Mean Shift and Optimal Prediction for Efficient Object Tracking. IEEE Inter-national Conference on Image Processing 3, 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)
Hue, C., Le Cadre, J., Perez, P.: Tracking Multiple Objects with Particle Filtering. IEEE Transactions on Aerospace and Electronic Systems 38, 313–318 (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)
Otsu, N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Trans.SMC. 9(1), 62–66 (1979)
Gao, T.: Liu, Z.-g.: Moving Video Object Segmentation based on Redundant Wavelet Transform. In: Proc. IEEE Int. Conf.on Information and Automation, pp. 156–160 (2008)
David, G.: Lowe: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
David, G.: Lowe: Object Recognition from Local Scale-Invariant Features. In: International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (1999)
Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for On-Line Nonlinear/ Nongaussian Bayesian Tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)
Pitt, M., Shephard, N.: Auxiliary Particle Filters. J. Amer. Statist. Assoc. 94(446), 590–599 (1999)
Doucet, A., Vo, B.-N., Andrieu, C., Davy, M.: Particle Filter for Multi-Target Tracking and Sensor Management. In: The Fifth International Conference on Information Fusion, pp. 474–481 (2002)
Sidenbladh, H.: Multi-target Particle Filtering for the Probability Hypothesis Density. In: The Sixth International Conference on Information Fusion, pp. 1110–1117 (2003)
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© 2009 Springer-Verlag Berlin Heidelberg
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Gao, T., Liu, Zg., Gao, Wc., Zhang, J. (2009). Moving Vehicle Tracking Based on SIFT Active Particle Choosing. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_85
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DOI: https://doi.org/10.1007/978-3-642-03040-6_85
Publisher Name: Springer, Berlin, Heidelberg
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