Project Title

boxmot — State-of-the-Art Multi-Object Tracking Modules for Advanced Computer Vision Tasks

Overview

BoxMOT is an open-source Python project that offers pluggable state-of-the-art (SOTA) multi-object tracking modules. It is designed to integrate seamlessly with segmentation, object detection, and pose estimation models that output bounding boxes. The project stands out for its pluggable architecture, universal model support, and benchmark-ready evaluation pipelines, making it a versatile tool for researchers and developers in the field of computer vision.

Key Features

Pluggable Architecture: Easily swap in/out SOTA multi-object trackers.
Universal Model Support: Integrates with any segmentation, object-detection, and pose-estimation models that output bounding boxes.
Benchmark-Ready: Local evaluation pipelines for MOT17, MOT20, and DanceTrack datasets with "official" ablation detectors.

Use Cases

Research and Development: Researchers can use BoxMOT to test and compare different multi-object tracking algorithms on various datasets.
Video Surveillance: BoxMOT can be employed in video surveillance systems to track multiple objects in real-time.
Automated Traffic Monitoring: The project can be utilized in traffic monitoring systems to track vehicles and pedestrians for traffic management.

Advantages

Performance Modes: Offers motion-only and motion + appearance modes for different performance and accuracy needs.
Reusable Detections & Embeddings: Saves time by allowing for the reuse of detections and embeddings, speeding up evaluations.
Extensive Benchmark Results: Provides detailed benchmark results for various trackers, aiding in the selection of the most suitable tracker for specific applications.

Limitations / Considerations

Computational Cost: The motion + appearance mode may require higher computational resources compared to the motion-only mode.
License: The project's license is currently unknown, which may affect its use in commercial applications.

DeepSORT: A deep learning-based multi-object tracking framework that differs in its approach to appearance feature extraction.
JDE (Joint Detection and Embedding): A real-time multi-object tracking framework that focuses on high-speed tracking.
MOTChallenge: A benchmark for multi-object tracking that provides datasets and evaluation metrics, but does not offer a tracking solution like BoxMOT.

Basic Information

GitHub: https://github.com/mikel-brostrom/boxmot
Stars: 7,700
License: Unknown
Last Commit: 2025-10-10

📊 Project Information

Project Name: boxmot
GitHub URL: https://github.com/mikel-brostrom/boxmot
Programming Language: Python
⭐ Stars: 7,700
🍴 Forks: 1,850
📅 Created: 2020-06-26
🔄 Last Updated: 2025-10-10

🏷️ Project Topics

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