mlsanityData to 14 Oct 2026

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

arXiv 2110.13444 ↗First posted 26 Oct 2021cs.CV cs.LG cs.ROcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

12 of 29 references.

References · 12

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 692
  3. 2020HOTA: A Higher Order Metric for Evaluating Multi-Object Trackingcited by 552
  4. 2016MOT16: A Benchmark for Multi-Object Trackingcited by 532
  5. 2019Multiple Futures Predictioncited by 178
  6. 2016Generalized optimal sub-pattern assignment metriccited by 33
  7. 2016A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithmscited by 17
  8. 2016Multiple target tracking based on sets of trajectoriescited by 11
  9. 2018Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectoriescited by 10
  10. 2016A metric for sets of trajectories that is practical and mathematically consistentcited by 7
  11. 2019Poisson Multi-Bernoulli Mixtures for Sets of Trajectoriescited by 4
  12. 2019Spooky effect in optimal OSPA estimation and how GOSPA solves itcited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 4.

6 papers.

  1. 2024TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Trackingcited by 2
  2. 2025From Target Tracking to Targeting Track -- Part I: A Metric for Spatio-Temporal Trajectory Evaluationcited by 2
  3. 2022Detection-aware multi-object tracking evaluationcited by 1
  4. 2025Fast computation of the TGOSPA metric for multiple target tracking via unbalanced optimal transportcited by 1
  5. 2025GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithmscited by 0
  6. 2025Model-based Multi-object Visual Tracking: Identification and Standard Model Limitationscited by 0