Abstract
For particle filtering tracking method, particle choosing was random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method was presented. The motion region was detected by redundant discrete wavelet transforms method (RDWT), and then the key points were obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method were 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 are more accurate and robust of occlusion.
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Gao, T., Liu, Zg., Zhang, J. (2009). Feature Particles Tracking for the Moving Object. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_7
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DOI: https://doi.org/10.1007/978-3-540-92814-0_7
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