BoxMOT Project Overview
BoxMOT is a cutting-edge platform designed to seamlessly integrate state-of-the-art multi-object tracking modules into models focused on segmentation, object detection, and pose estimation. This innovative tool is geared towards enhancing the capabilities of various detection models, including popular ones like YOLOv8, YOLOv9, and YOLOv10. It provides users with an array of tracking options that cater to different computational constraints, ensuring flexibility across diverse hardware environments, from basic CPUs to advanced GPUs.
Key Features of BoxMOT
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Pluggable Trackers: The platform supports an impressive variety of multi-object tracking algorithms, allowing users to choose the one that best suits their needs. Available options include top performers such as botsort, strongsort, bytetrack, and more. Each tracker comes with unique strengths as evaluated by metrics like HOTA, MOTA, and IDF1.
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ReID Integration: For those interested in appearance-based tracking, BoxMOT offers both heavyweight and lightweight re-identification (ReID) models. These models help in distinguishing between objects that appear similar, thus improving tracking accuracy.
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Vast Compatibility: BoxMOT can be employed with a wide range of object detection frameworks and formats, facilitating versatile applications across different media types, such as videos, images, streaming sources, and more.
Why Choose BoxMOT?
One of the standout advantages of BoxMOT is its adaptability to the user's computing environment. Whether you're working with robust GPU clusters or constrained by CPU limits, BoxMOT provides tracking solutions that fit your setup. Additionally, it aids in speeding up experimental processes by storing and reusing detections and embeddings, mitigating the need for repetitive data generation.
Getting Started
To utilize BoxMOT, users should start with a Python environment (version 3.9 or higher). The installation process is straightforward:
- Clone the BoxMOT repository and navigate into the directory.
- Install dependencies using the Poetry package manager.
- Activate the environment to start experimenting with YOLO models and tracking modules.
For those only interested in the tracking modules, BoxMOT can be quickly installed via pip.
Practical Examples
BoxMOT was designed with usability in mind, offering practical examples to help users get started with tracking tasks. These examples include:
- Demonstrating the use of YOLO models for bounding box tracking.
- Employing ReID models for appearance-based identity tracking.
- Leveraging BoxMOT with different input sources such as camera streams, videos, and images.
Evaluation and Optimization
BoxMOT enables comprehensive evaluation of various detector-tracker-ReID model combinations. This is particularly useful for standard datasets like MOT17 or custom datasets. In addition, an evolutionary strategy based on a multi-objective genetic algorithm is implemented for fine-tuning tracker parameters, ensuring optimal performance across different scenarios.
Exporting and Custom Models
For advanced users, BoxMOT supports the export of ReID models into various formats such as ONNX, OpenVINO, TorchScript, and TensorRT, further enhancing its integration capabilities in larger AI workflows.
BoxMOT also provides notebooks for custom tracking examples, showcasing the integration with popular platforms like TorchVision for bounding box, pose, and segmentation tracking.
Collaborative and Supportive Environment
BoxMOT is backed by a community of contributors and offers extensive support through GitHub for tracking bugs and feature requests. Collaboration and professional support inquiries are welcomed via email, ensuring a comprehensive support ecosystem for users.
In summary, BoxMOT stands out as a versatile, high-performance solution for integrating cutting-edge tracking algorithms into various detection models, facilitating seamless and efficient object tracking across diverse applications.