A Moving Window Based Approach to Multi-scan Multi-Target Tracking

TL;DR

This paper introduces a moving window approach to multi-target tracking that efficiently updates only recent association maps using the GLMB smoother, balancing accuracy and computational load for practical applications.

Contribution

It proposes a novel moving window based method for multi-target tracking with the GLMB smoother, reducing computational complexity while maintaining effective tracking performance.

Findings

Significant reduction in computational load compared to full smoothing.

Maintains high tracking accuracy with limited recent data updates.

Applicable to real-time multi-target tracking scenarios.

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.