This library provides a Python implementation of CLEAR MOT metrics for evaluation object tracker performances based on
Bernardin, Keni, and Rainer Stiefelhagen. "Evaluating multiple object tracking performance: the CLEAR MOT metrics."
Main features are
- Distance agnostic. Supports Euclidean, Intersection over Union and other distances measures.
- Complete event history. Tracks all relevant per-frame events suchs as correspondences, misses, false alarms and switches.
- Uses Python pandas for data structures and analysis.
- Supports MOTA and MOTP metrics.
- Global minimum cost assignments are accomplished through Kuhn-Munkres algorithm.
import clearmot as cm
# TODOTo install py-clearmot clone this repository and use pip to install
from local sources.
pip install -e <path/to/setup.py>
Python 3.5/3.6 and numpy, pandas and scipy is required.
| Branch | Status |
|---|---|
| master |  |
| develop |  |
MIT License
Copyright (c) 2017 Christoph Heindl
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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