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We present a feature-aided approach to multiple-target tracking built upon tree-search tracking algorithms. Using a search tree to represent the target state space, the tracker navigates the tree to identify the most likely sequence of states visited by the target(s). The search for new targets and for new states of existing targets is governed by path metrics that are proportional to the posterior...
We propose a novel approach to tracking a target in clutter based on the stack algorithm for tree search. The proposed tracking approach reduces the size of the search tree by employing a coarse discretization of the target state space. To reduce the quantization error that results from coarse discretization, the representative value of each quantized region is sampled from an estimated importance...
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