The Smooth Trajectory Estimator for LMB Filters
arXiv:2401.00682 · doi:10.1109/ICCAIS59597.2023.10382267
Abstract
This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter. The source code is publicly available at https://tinyurl.com/ste-lmb, aimed at promoting advancements in MOT algorithms.
arXiv comment6 pages, 5 figures. Presented at The 12th IEEE International Conference on Control, Automation and Information Sciences (ICCAIS 2023), Nov 2023, Hanoi, Vietnam
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