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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...
In this paper, we present a method for 3-dimensional tracking using interaction matrix with a stereo visual sensing system. We developed two constraints to avoid the singularity and local minima of the interaction matrix. These constraints were combined with the minimization of tracking errors to identify suitable system configuration for optimal tracking. Experiments were conducted to validate the...
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