GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms

TL;DR

This paper proposes two new quasi-metrics, GOSPA and T-GOSPA, for evaluating multi-object tracking algorithms, allowing flexible penalization of errors and including track switching costs.

Contribution

The paper introduces novel GOSPA and T-GOSPA quasi-metrics with flexible error penalization and asymmetric localization costs for better MOT performance assessment.

Findings

T-GOSPA effectively measures trajectory discrepancies in simulations.

The proposed quasi-metrics include track switching costs.

Simulations demonstrate the metrics' ability to evaluate Bayesian MOT algorithms.

Abstract

This paper introduces two quasi-metrics for performance assessment of multi-object tracking (MOT) algorithms. One quasi-metric is an extension of the generalised optimal subpattern assignment (GOSPA) metric and measures the discrepancy between sets of objects. The other quasi-metric is an extension of the trajectory GOSPA (T-GOSPA) metric and measures the discrepancy between sets of trajectories. Similar to the GOSPA-based metrics, these quasi-metrics include costs for localisation error for properly detected objects, the number of false objects and the number of missed objects. The T-GOSPA quasi-metric also includes a track switching cost. Differently from the GOSPA and T-GOSPA metrics, the proposed quasi-metrics have the flexibility of penalising missed and false objects with different costs, and the localisation costs are not required to be symmetric. We also explain how to obtain similarity score functions based on these quasi-metrics. The performance of several Bayesian MOT algorithms is assessed with the T-GOSPA quasi-metric via simulations.