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BoxMOT demo

mikel-brostrom%2Fboxmot | Trendshift

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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, 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, 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.
  • Public Python API for embedding the same workflows in applications and notebooks.

Installation

BoxMOT supports Python 3.9 through 3.12.

pip install boxmot
boxmot --help

For mode-specific extras such as yolo, evolve, research, onnx, openvino, and tflite, see the installation guide.

Benchmark Results (MOT17 ablation split)

Tracker Status OBB HOTA↑ MOTA↑ IDF1↑ FPS
botsort 69.418 78.232 81.812 12
boosttrack 69.253 75.914 83.206 13
strongsort 68.05 76.185 80.763 11
deepocsort 67.796 75.868 80.514 12
bytetrack 67.68 78.039 79.157 720
hybridsort 67.39 74.127 79.105 25
ocsort 66.441 74.548 77.899 890
sfsort 62.653 76.87 69.184 6000

Evaluation was run on the second half of the MOT17 training set because the validation split is not public and the ablation detector was trained on the first half. Results used pre-generated detections and embeddings with each tracker configured from its default repository settings.

Reproduction details and evaluation semantics live in:

Minimal Usage

CLI:

boxmot track --detector yolov8n --reid osnet_x0_25_msmt17 --tracker botsort --source video.mp4 --save

Python:

from boxmot import Boxmot

run = Boxmot(detector="yolov8n", reid="osnet_x0_25_msmt17", tracker="botsort").track(
    source="video.mp4",
    save=True,
)
print(run)

Contributing

Start with CONTRIBUTING.md and the contributor docs.

Contributors

BoxMOT contributors

Support and Citation