cheind/py-motmetrics

:bar_chart: Benchmark multiple object trackers (MOT) in Python

What it solves

It provides a standardized way to evaluate the performance of multiple object tracking (MOT) systems. It ensures that tracking results are measured using industry-standard metrics, allowing researchers and developers to compare different tracking algorithms against ground truth data.

How it works

The library implements a set of tracking metrics including CLEAR MOT, Identity, and HOTA-related measures. It can automatically detect and load data from common formats like MOTChallenge, VATIC, and UA-DETRAC. It uses a MOTAccumulator to store frame-level matching events and provides distance helpers (such as IoU and Euclidean matrices) to calculate how well predictions match the ground truth.

Who it’s for

Computer vision engineers and researchers working on multi-object tracking systems who need to align their evaluation results with benchmarks like MOTChallenge.

Highlights

  • Standardized Metrics: Implements MOTA, MOTP, IDF1, and HOTA.
  • Broad Format Support: Automatically detects MOTChallenge, VATIC, and idée UA-DETRAC formats.
  • Easy Integration: Offers a high-level evaluate_motchallenge function for quick summaries and a command-line evaluator.
  • Parity Checks: Includes tests to ensure results match the TrackEval tool.

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