motmetrics
Metrics for multiple object tracker benchmarking.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for academic, commercial, and proprietary projects.
last release 2022-12-26 (1327 days) · last repo commit 2026-07-17 · 1,486 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 127,585 downloads/mo, #11,737 on PyPI
Alternatives
Verify before relying
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']
)- 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
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 on it.
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
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
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpypandasscipyxmltodictenum34 |
| Maintenance | Actively maintained 1,327 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 127,585 / month, #11,737 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: motmetrics-1.4.0-py3-none-any.whl
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