mikel-brostrom/boxmot

Health v2 · maintenance only

Adoption not part of the score

8281 stars · 1917 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

BoxMOT is a pluggable Python and C++ library providing state-of-the-art multi-object tracking (MOT) algorithms such as ByteTrack, BoT-SORT, StrongSORT, OC-SORT, DeepOCSORT, and BoostTrack, supporting both axis-aligned and oriented bounding boxes. It offers a unified CLI and Python API covering tracking, evaluation, tuning, ReID training, and export workflows with swappable detectors and trackers.

Use cases

• track multiple objects in a video with yolo detections
• compare mot trackers on mot17 benchmark
• run bytetrack or botsort on my own detector output
• train and evaluate a person re-identification model
• track objects with oriented bounding boxes
• deploy a c++ multi-object tracker in production
• tune tracker hyperparameters for my dataset
• export reid embeddings for offline tracking

When to choose

• you need state-of-the-art multi-object tracking with swappable trackers and detectors
• you want a single CLI/API for tracking, evaluation, tuning, and ReID workflows
• you need oriented bounding box tracking or a native C++ tracker backend

When to avoid

• you only need single-object tracking
• you need a permissively licensed library (it is AGPL-3.0)
• you need real-time tracking on edge devices without GPU or TensorRT support

Facets

library · maturity active

machine-learning computer-vision cli sdk computer-vision machine-learning deep-learning python cpp cross-platform cli multi-object-tracking tracking-by-detection bytetrack botsort strongsort ocsort deepocsort boosttrack reid yolo tensorrt oriented-bounding-boxes mots video docker

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