Receding Horizon Estimation for Multi-Target Tracking via Random Finite Set Approach
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© 2018 ISIF This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists.
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Vo, Ba Tuong; Vo, Ba-Ngu (2018)© 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the ...
Vo, Ba-Ngu; Vo, Ba Tuong; Reuter, S.; Lam, Q.; Dietmayer, K. (2014)Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving ...
Ristic, B.; Vo, Ba-Ngu; Clark, D.; Vo, Ba Tuong (2011)Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the ...