A Solution for Large-scale Multi-object Tracking
Michael Beard, Ba Tuong Vo, Ba-Ngu Vo

TL;DR
This paper introduces a large-scale multi-object tracking algorithm based on the GLMB filter, capable of handling over a million objects with high accuracy despite false alarms and measurement uncertainties.
Contribution
It presents a novel large-scale multi-object tracking method using the GLMB filter, including a new evaluation strategy for large scenarios.
Findings
Successfully tracked over one million objects in simulation.
Developed an efficient large-scale OSPA metric evaluation method.
Demonstrated robustness against false alarms and measurement errors.
Abstract
A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as misdetections and measurement origin uncertainty due to closely spaced objects. The algorithm is demonstrated on a simulated large-scale tracking scenario, where the peak number objects appearing simultaneously exceeds one million. To evaluate the performance of the proposed tracker, we also introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for its evaluation in large-scale scenarios.
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