An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Journal article, 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.



Task analysis

Signal processing algorithms

Approximation algorithms

Multitarget tracking

Measurement uncertainty

Uncertainty evaluation

Performance measure

Multi-object tracking

Density measurement


Juliano Pinto

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE Signal Processing Letters

1070-9908 (ISSN) 15582361 (eISSN)

Vol. 28 1689-1693 9512514

Areas of Advance

Information and Communication Technology

Subject Categories

Communication Systems

Signal Processing

Computer Science



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Latest update

4/5/2022 6