English

A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms

Computer Vision and Pattern Recognition 2021-10-27 v1 Machine Learning Robotics Applications

Abstract

This paper proposes a metric for sets of trajectories to evaluate multi-object tracking algorithms that includes time-weighted costs for localisation errors of properly detected targets, for false targets, missed targets and track switches. The proposed metric extends the metric in [1] by including weights to the costs associated to different time steps. The time-weighted costs increase the flexibility of the metric [1] to fit more applications and user preferences. We first introduce a metric based on multi-dimensional assignments, and then its linear programming relaxation, which is computable in polynomial time and is also a metric. The metrics can also be extended to metrics on random finite sets of trajectories to evaluate and rank algorithms across different scenarios, each with a ground truth set of trajectories.

Keywords

Cite

@article{arxiv.2110.13444,
  title  = {A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms},
  author = {Ángel F. García-Fernández and Abu Sajana Rahmathullah and Lennart Svensson},
  journal= {arXiv preprint arXiv:2110.13444},
  year   = {2021}
}

Comments

Matlab code available at https://github.com/Agarciafernandez/MTT (Trajectory metric folder)