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• W2413883933 abstract "The Probability Hypothesis Density (PHD) filter is an efficient formulation of multi-target state estimation that circumvents the combinatorial explosion of the multi-target posterior by operating on single-target space without maintaining target identities. In this paper, we propose a multi-target tracker based on the PHD filter that provides instantaneous state estimation and delayed decision on data association. For this purpose, we reformulate the PHD recursion in terms of single-target track hypotheses and solve a min-cost flow network for trajectory estimation where measurement likelihoods and transition probabilities are based on multi-target state estimates. In this manner, the presented approach combines global data association with efficient multi-target filtering. We evaluate the approach on a publicly available pedestrian tracking dataset to present state estimation and data association capabilities." @default.
• W2413883933 created "2016-06-24" @default.
• W2413883933 creator A5008718172 @default.
• W2413883933 creator A5083165697 @default.
• W2413883933 date "2016-05-01" @default.
• W2413883933 modified "2023-09-24" @default.
• W2413883933 title "Global data association for the Probability Hypothesis Density filter using network flows" @default.
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• W2413883933 doi "https://doi.org/10.1109/icra.2016.7487180" @default.
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