motmetrics

What it is and what it does

py-motmetrics is a Python library for evaluating multiple object tracker performance against ground truth annotations. It implements standard MOT metrics (MOTA, MOTP) and global ID measures (IDF1, IDP, IDR) used in the MOTChallenge benchmarks. The library handles the core challenge of multi-object tracking evaluation: finding optimal correspondences between predicted and ground truth objects across video frames, accounting for false positives, misses, and identity switches.

The package uses pandas DataFrames to track per-frame events (matches, false alarms, track switches) and supports pluggable assignment solvers (scipy, ortools, munkres). Results are compatible with MOTChallenge output formats, making it useful for comparing tracker implementations or validating new detection/tracking pipelines against published benchmarks.

Use it for:

• Benchmark a custom multi-object tracker against MOTChallenge datasets to compare MOTA/MOTP scores
• Evaluate pedestrian or vehicle detection and tracking pipelines in autonomous driving or surveillance systems
• Compare multiple tracker implementations on the same ground truth to identify which performs best
• Generate detailed per-frame event logs (false positives, identity switches) to debug tracker failures
• Integrate tracker evaluation into a continuous testing pipeline for computer vision models

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Computes multiple object tracker (MOT) evaluation metrics including MOTA, MOTP, and ID measures by matching ground truth objects to predictions across video frames.

Yes. The package fills a specific, well-defined role in multi-object tracking evaluation with low install friction, active maintenance, no security vulnerabilities, and permissive licensing. It is the standard choice for MOTChallenge-compatible benchmarking. Install it if you need to evaluate or compare object trackers; skip it if your use case does not involve multi-object tracking metrics.

Install

motmetrics on PyPI

pip install motmetrics

uv add motmetrics

poetry add motmetrics

Installing motmetrics

Before you install

Low friction: pure Python wheel with five standard scientific dependencies (numpy, pandas, scipy, xmltodict, enum34). Repository is actively maintained with recent commits and no known vulnerabilities.

License in practice

MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for academic, commercial, and proprietary projects.

Quickstart

pip install motmetrics

import motmetrics as mm
import numpy as np

# Create metrics handler
mh = mm.metrics.create()

# Compute metrics given ground truth and predictions
metrics = mh.compute(
    gt,  # ground truth objects per frame
    dt,  # detected/predicted objects per frame
    metrics=['mota', 'motp', 'idf1']
)

Verify before relying

• Whether the package works with modern Python versions beyond 3.5/3.6/3.9 (requires_python field is empty in metadata)
• Exact solver backend availability and auto-tuning behavior for scipy, ortools, and munkres
• Whether xmltodict is used for MOTChallenge format parsing or is an unused legacy dependency

Package facts

Evidence: motmetrics-1.4.0-py3-none-any.whl

Keywords: tracker, MOT, evaluation, metrics, compare

Tags

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