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:bar_chart: Benchmark multiple object trackers (MOT) in Python
| Date | Stars |
|---|---|
| 2026-07-24 | 1484 |
| 2026-07-25 | 1484 |
| 2026-07-28 | 1484 |
| 2026-07-30 | 1484 |
| 2026-08-06 | 1484 |
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growth rate 0.00%/day
[](https://badge.fury.io/py/motmetrics) [](https://github.com/cheind/py-motmetrics/actions/workflows/python-package.yml) [](https://doi.org/10.5281/zenodo.14014773)
# py-motmetrics
**py-motmetrics** provides Python tools for evaluating multiple object tracking (MOT) results. It implements MOTChallenge-aligned CLEAR MOT, Identity, and HOTA-related metrics, including MOTA, MOTP, IDF1, precision, recall, and track quality counts.
## Installation
```bash
pip install motmetrics
```
Python 3.8 through 3.14 is supported.
For development:
```bash
uv venv
uv pip install --group dev
```
## Quick Start
For MOTChallenge-style text files, compute and print metrics in one call. Supported file formats are detected automatically.
```python
import motmetrics as mm
summary = mm.evaluate_motchallenge("path/to/gt.txt", "path/to/pred.txt")
print(summary)
```
`summary` displays as a MOTChallenge-style table and keeps the raw pandas data available:
```python
summary.mota
summary.idf1
summary.hota
summary.df.to_csv("metrics.csv")
```
By default, `evaluate_motchallenge` uses `fmt="auto"`. It detects MOTChallenge text, VATIC text, and UA-DETRAC `.mat`/`.xml` files. For ambiguous text files, pass the format explicitly:
```python
summary = mm.evaluate_motchallenge(gt, pred, fmt=mm.io.Format.MOT16)
```
Folder evaluation uses the same function:
```python
summary = mm.evaluate_motchallenge("path/to/gt_root", "path/to/preds_root")
print(summary)
```
Expected folder layout:
```text
gt_root/<SEQUENCE>/gt/gt.txt
preds_root/<SEQUENCE>.txt
```
The command-line evaluator is still available:
```bash
python -m motmetrics.apps.eval_motchallenge path/to/gt_root path/to/preds_root
```
## Metrics
List all registered metrics:
```python
import motmetrics as mm
print(mm.list_metrics_markdown())
```
The default MOTChallenge summary includes the commonly reported CLEAR, Identity, and HOTA metrics.
## Advanced Use
Useful lower-level pieces:
- `mm.MOTAccumulator` stores frame-level matching events.
- `mm.distances` contains distance helpers such as IoU and Euclidean matrices.
- `mm.io.loadtxt(..., fmt="auto")` detects MOTChallenge text, VATIC text, and UA-DETRAC MAT/XML files.
- `mm.metrics.create()` returns a `MetricsHost` for custom metric selection.
- `mm.utils.compare_to_groundtruth` compares loaded dataframes directly.
- `mm.utils.compare_to_groundtruth_reweighting` supports custom HOTA-style multi-threshold workflows.
For the full HOTA/CLEAR/Identity parity check against TrackEval, see [motmetrics/tests/test_trackeval_parity.py](motmetrics/tests/test_trackeval_parity.py).
## MOTChallenge Notes
Results are aligned with the MOTChallenge devkit, with two naming/format differences:
- `FAR` is not listed directly; it can be computed as false positives per frame.
- MOTChallenge reports MOTP as a percentage, while py-motmetrics reports the average distance. Convert with `(1 - MOTP) * 100` for MOTChallenge-style MOTP.
## Development
Run the test suite:
```bash
uv run --no-project pytest
```
Run the TrackEval parity test locally:
```bash
uv pip install trackeval==1.3.0
uv run --no-project pytest -q motmetrics/tests/test_trackeval_parity.py
```
## References
1. Bernardin, Keni, and Rainer Stiefelhagen. "Evaluating multiple object tracking performance: the CLEAR MOT metrics." EURASIP Journal on Image and Video Processing, 2008.
2. Milan, Anton, et al. "MOT16: A benchmark for multi-object tracking." arXiv preprint arXiv:1603.00831, 2016.
3. Li, Yuan, Chang Huang, and Ram Nevatia. "Learning to associate: HybridBoosted multi-target tracker for crowded scene." CVPR, 2009.
4. Ristani, Ergys, et al. "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking." ECCV Workshop, 2016.
## License
MIT. See [LICENSE](LICENSE).Excerpt of 3,990 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8ff3cd17937eac71, topic:object-detection, topic:object-tracking, readme:object tracking
matched fp:8ff3cd17937eac71, topic:benchmark