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

arXiv cs.CV / 4/27/2026

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Key Points

The paper proposes two new quasi-metrics to evaluate multi-object tracking (MOT) performance: an object-set version extending GOSPA (GOSPA-like) and a trajectory-set version extending T-GOSPA (T-GOSPA-like).
Both quasi-metrics account for localization error, false-object counts, and missed-object counts, while the trajectory-based metric additionally includes a track switching cost.
Unlike standard GOSPA/T-GOSPA, the proposed quasi-metrics allow different penalties for missed vs. false objects and permit asymmetric localization costs.
The authors show how to derive similarity score functions from these quasi-metrics and validate the approach by evaluating several Bayesian MOT algorithms using T-GOSPA in simulations.

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.