Evaluation and Postprocessing

Use this guide when you need to interpret benchmark outputs from boxmot eval, Boxmot.val(...), tune, or research.

Core metrics

• HOTA for overall tracking quality
• MOTA for CLEAR-style summary quality
• IDF1 for identity consistency
• AssA and AssRe for association quality
• IDSW and IDs for identity-switch context

Where metrics appear

• eval reports benchmark results directly
• tune uses validation results to score parameter trials
• research optimizes code changes against combined benchmark summaries

For raw runtime summaries from the Python API, evaluate(...) aggregates counts and timings but does not replace TrackEval ground-truth evaluation.

Postprocessing modes

eval supports three postprocessing modes through --postprocessing:

• none
• gsi for Gaussian-smoothed interpolation
• gbrc for gradient-boosting-based reconnection and interpolation

Common commands

boxmot eval --benchmark mot17-ablation --tracker boosttrack
boxmot eval --benchmark mot17-ablation --tracker boosttrack --postprocessing gsi
boxmot eval --benchmark mot17-ablation --tracker boosttrack --postprocessing gbrc

Main outputs

• combined benchmark metrics such as HOTA, MOTA, and IDF1
• per-sequence summaries
• MOT-style tracker outputs
• reused cache paths and evaluation artifacts in the run directory

See Results and Artifacts.