A Moving Window Based Approach to Multi-scan Multi-Target Tracking
- URL: http://arxiv.org/abs/2210.04008v1
- Date: Sat, 8 Oct 2022 12:37:27 GMT
- Title: A Moving Window Based Approach to Multi-scan Multi-Target Tracking
- Authors: Diluka Moratuwage, Changbeom Shim, and Yuthika Punchihewa
- Abstract summary: Multi-target state estimation refers to estimating the number of targets and their trajectories in a surveillance area.
We propose a moving window based solution for multi-target tracking using the Generalized Labeled Multi-Bernoulli (GLMB) smoother.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-target state estimation refers to estimating the number of targets and
their trajectories in a surveillance area using measurements contaminated with
noise and clutter. In the Bayesian paradigm, the most common approach to
multi-target estimation is by recursively propagating the multi-target
filtering density, updating it with current measurements set at each timestep.
In comparison, multi-target smoothing uses all measurements up to current
timestep and recursively propagates the entire history of multi-target state
using the multi-target posterior density. The recent Generalized Labeled
Multi-Bernoulli (GLMB) smoother is an analytic recursion that propagate the
labeled multi-object posterior by recursively updating labels to measurement
association maps from the beginning to current timestep. In this paper, we
propose a moving window based solution for multi-target tracking using the GLMB
smoother, so that only those association maps in a window (consisting of latest
maps) get updated, resulting in an efficient approximate solution suitable for
practical implementations.
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