An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Artikel i vetenskaplig tidskrift, 2021

Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models which produce uncertainty estimates of lower quality, negatively impacting any downstream systems that rely on them. Additionally, most MOT performance measures have hyperparameters, which makes comparisons of different trackers less straightforward. We propose the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of ground-truth objects as a performance measure. This measure takes into account all available uncertainty information in a sound mathematical manner without hyperparameters. We provide efficient algorithms for approximating the computation of the NLL for several common MOT algorithms, show that in some cases it decomposes and approximates the widely-used GOSPA metric, and provide several illustrative examples highlighting the advantages of the NLL in comparison to other MOT performance measures.

Performance measure

Measurement uncertainty

Uncertainty evaluation


Signal processing algorithms

Task analysis

Multitarget tracking

Multi-object tracking

Density measurement

Approximation algorithms



Juliano Pinto

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

IEEE Signal Processing Letters

1070-9908 (ISSN)

Vol. 28 1689-1693




Datavetenskap (datalogi)



Mer information

Senast uppdaterat