Pluggable Python and C++ multi-object tracking modules for axis-aligned and oriented bounding box detections from any model.
Docs • Installation • Modes • API Reference • Trackers • Contributing
BoxMOT gives you one CLI and one Python API for running modern multi-object tracking workflows. It covers direct tracking, catalog-backed evaluation, tuning, research loops, ReID training and evaluation, and ReID export without forcing you to rebuild the detector and tracker stack for each experiment.
Why BoxMOT
- One interface for
track,generate,eval,tune,research,train-reid,eval-reid,export, and nativebuildworkflows. - Swappable trackers with shared detector and ReID plumbing.
- Dataset and experiment workflows with reusable detections and embeddings.
- Support for both AABB and OBB tracking paths.
- Optional production-ready native C++ tracker implementations with the same metrics as the Python path, opted into via
--tracker-backend cppand embeddable in standalone C++ projects via CMake (see Native C++ Integration). - Public Python API for embedding the same workflows in applications and notebooks.
Installation
BoxMOT supports Python 3.10 through 3.13.
pip install boxmot
boxmot --help
For mode-specific extras such as yolo, evolve, research, onnx, openvino, and tflite, see the installation guide.
Benchmark Results
| Tracker | Status | MOT17 ablation | SportsMOT val | MMOT OBB test | OBB | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HOTA | MOTA | IDF1 | HOTA | MOTA | IDF1 | HOTA | MOTA | IDF1 | |||
| occluboost | ✅ | 71.10 (71.10) |
78.50 (78.50) |
85.28 (85.28) |
83.17 | 97.48 | 89.36 | 49.84 (49.84) |
39.41 (39.41) |
58.60 (58.60) |
✅ |
| botsort | ✅ | 69.68 (69.74) |
78.23 (78.27) |
82.33 (82.55) |
76.93 | 98.11 | 78.30 | 52.31 (52.40) |
45.43 (45.53) |
61.42 (61.42) |
✅ |
| boosttrack | ✅ | 69.25 (—) |
75.91 (—) |
83.20 (—) |
76.32 | 97.08 | 77.82 | 48.39 (—) |
41.36 (—) |
56.36 (—) |
✅ |
| strongsort | ✅ | 68.05 (—) |
76.19 (—) |
80.76 (—) |
79.80 | 97.31 | 80.27 | 49.76 (—) |
43.70 (—) |
57.32 (—) |
✅ |
| deepocsort | ✅ | 67.95 (—) |
75.83 (—) |
80.54 (—) |
79.51 | 97.94 | 79.59 | 50.84 (—) |
44.21 (—) |
59.33 (—) |
✅ |
| bytetrack | ✅ | 67.68 (67.68) |
78.04 (78.04) |
79.16 (79.16) |
67.93 | 97.25 | 76.90 | 33.97 (33.97) |
33.72 (33.72) |
39.74 (39.74) |
✅ |
| hybridsort | ✅ | 67.31 (—) |
74.09 (—) |
78.87 (—) |
81.14 | 98.07 | 81.88 | 54.64 (—) |
47.50 (—) |
64.67 (—) |
✅ |
| ocsort | ✅ | 66.44 (66.44) |
74.55 (74.55) |
77.90 (77.90) |
76.34 | 96.60 | 75.64 | 28.64 (28.64) |
26.17 (26.17) |
30.06 (30.06) |
✅ |
| sfsort | ✅ | 62.65 (62.65) |
76.87 (76.87) |
69.18 (69.18) |
75.73 | 98.39 | 72.99 | 47.83 (47.83) |
45.42 (45.42) |
52.09 (52.09) |
✅ |
Scores are Python first and C++ in parentheses.
MMOT reported metrics are 'class average'. See Experiment Workflows for details.
Related guides:
Minimal Usage
CLI:
boxmot track --detector yolo26n --reid lmbn_n_duke --tracker occluboost --source 0 --save --show
Python:
import numpy as np
from boxmot.trackers import OccluBoost
tracker = OccluBoost()
# dets: (N, 6) array with [x1, y1, x2, y2, conf, cls] per detection
dets = np.array([[100, 200, 300, 400, 0.9, 0]], dtype=np.float32)
# OBB alternative: (N, 7) with [cx, cy, w, h, angle_radians, conf, cls]
# dets = np.array([[200, 300, 200, 200, 0.25, 0.9, 0]], dtype=np.float32)
img = np.zeros((480, 640, 3), dtype=np.uint8) # current frame
# tracks: AABB (M, 8), or OBB (M, 9) with angle after h
tracks = tracker.update(dets, img)
print(tracks)
Contributing
Start with CONTRIBUTING.md and the contributor docs.
Contributors
Support and Citation
- Bugs and feature requests: GitHub Issues
- Questions and discussion: GitHub Discussions or Discord
- Citation metadata: CITATION.cff
- Commercial support:
box-mot@outlook.com
1 maintainer
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