{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/14"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"BoxMOT provides pluggable multi-object tracking modules that work with bounding box detections from any model, supporting both axis-aligned and oriented bounding boxes through a unified Python API and CLI.","skillfed_tags":["computer-vision","tracking","benchmarking"],"use_cases":["Track pedestrians, vehicles, or sports players across video frames using detections from YOLO or another detector.","Benchmark tracker performance on standard datasets (MOT17, SportsMOT, MMOT) with cached workflows to avoid recomputing detections.","Train and evaluate ReID (re-identification) models to improve tracking accuracy on specific object classes or domains.","Embed multi-object tracking in a Python application or Jupyter notebook without building a custom tracker from scratch.","Export a production-ready C++ tracker implementation for deployment in standalone applications via CMake.","Experiment with different tracker algorithms and hyperparameters using the CLI or Python API without modifying detector/ReID code."],"what_it_does":"BoxMOT is a multi-object tracking framework that bridges detection and tracking workflows. It provides a unified interface for running tracking experiments, benchmarking, ReID training, and production inference across multiple tracker implementations (OccluBoost, ByteTrack, BoTSORT, and others). The package handles both axis-aligned and oriented bounding box tracking, supports swappable detector and ReID components, and offers both a Python API for embedding in applications and a CLI for standalone use.\n\nThe core workflow is tracking-by-detection: you feed it detections (bounding boxes with confidence scores) frame-by-frame, and it assigns consistent IDs across frames using motion and appearance cues. It includes benchmark-oriented features like cached detection/embedding reuse to avoid redundant computation across experiments, native C++ tracker implementations for production deployment, and integrated ReID training pipelines. Dependencies are substantial (torch, torchvision, opencv-python, scikit-learn, timm) but standard for computer vision; the package itself is pure Python and installs with low friction.","worth_installing":"Yes, with AGPL-3.0 license caveat. BoxMOT is actively maintained, has low install friction, and offers a comprehensive, well-benchmarked tracking framework suitable for research, benchmarking, and production use. The AGPL-3.0 license requires that any modifications or derivative works be open-source; if you need proprietary tracking or closed-source modifications, this is not the right choice. For academic research, open-source projects, or applications where AGPL-3.0 compliance is acceptable, it is worth installing."},"id":"boxmot","links":{"html":"https://skillfed.io/packages/boxmot","md":"https://skillfed.io/packages/boxmot.md","pypi":"https://pypi.org/project/boxmot/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-10","license_spdx":null,"license_treatment":"agpl","name":"boxmot","python_support":"supports_current","summary":"BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models"},"popularity":{"monthly_downloads":162881,"position":10583,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"22.0.0"}