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• W2151502775 abstract "In this paper, we propose a new method to fuse sensing data of the most current observation into a 3-D visual tracker using pseudolikelihood functions with particle filtering techniques. With the proposed approach, the importance density function in particle filter can be modified to represent posterior states by particle crowds in a better way. Thus, it makes the tracking system more robust to noise and outliers. On the other hand, because the particle interpretation is performed in a much more efficient fashion, the number of particles used in tracking is greatly reduced, which improves the real-time performances of the system. Simulation and experimental results verified the effectiveness of the proposed method." @default.
• W2151502775 created "2016-06-24" @default.
• W2151502775 creator A5021100880 @default.
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• W2151502775 date "2009-08-01" @default.
• W2151502775 modified "2023-09-26" @default.
• W2151502775 title "Enhanced Particles With Pseudolikelihoods for Three-Dimensional Tracking" @default.
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• W2151502775 doi "https://doi.org/10.1109/tie.2009.2024099" @default.
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