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

Code

• github.com/Agarciafernandez/MTT
  OfficialIn papernone★ 155
• github.com/agarciafernandez/t-gospa-metric-python
  none★ 3

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.

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Reproductions