BoxMOT gives you one CLI and one Python API for running modern multi-object tracking workflows. It covers direct tracking, cached benchmark evaluation, tuning, research loops, ReID training and evaluation, and ReID export without forcing you to rebuild the detector and tracker stack for each experiment.

• One interface for track, generate, eval, tune, research, train, eval-reid, and export.
• Swappable trackers with shared detector and ReID plumbing.
• Benchmark-oriented 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 cpp and embeddable in standalone C++ projects via CMake (see Native C++ Integration).
• Public Python API for embedding the same workflows in applications and notebooks.

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.

Related guides:

• Evaluation and Postprocessing
• Benchmark Workflows
• Native C++ Integration

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)
img = np.zeros((480, 640, 3), dtype=np.uint8)  # current frame

# tracks: (M, 8) array with [x1, y1, x2, y2, id, conf, cls, det_ind] per track
tracks = tracker.update(dets, img)
print(tracks)

Start with CONTRIBUTING.md and the contributor docs.

• Bugs and feature requests: GitHub Issues
• Questions and discussion: GitHub Discussions or Discord
• Citation metadata: CITATION.cff
• Commercial support: box-mot@outlook.com