matrixgame2018 / mmtracking

OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

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MMTracking is an open source video perception toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch1.5+.

Major features

  • The First Unified Video Perception Platform

    We are the first open source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation.

  • Modular Design

    We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules.

  • Simple, Fast and Strong

    Simple: MMTracking interacts with other OpenMMLab projects. It is built upon MMDetection that we can capitalize any detector only through modifying the configs.

    Fast: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations.

    Strong: We reproduce state-of-the-art models and some of them even outperform the official implementations.


This project is released under the Apache 2.0 license.


Release QDTrack pretrained models.

v0.12.0 was released in 01/04/2022. Please refer to for details and release history.

Benchmark and model zoo

Results and models are available in the model zoo.

Supported methods of video object detection:

Supported methods of multi object tracking:

Supported methods of single object tracking:

Supported methods of video instance segmentation:


Please refer to for install instructions.

Getting Started

Please see and for the basic usage of MMTracking. We also provide usage tutorials, such as learning about configs, an example about detailed description of vid config, an example about detailed description of mot config, an example about detailed description of sot config, customizing dataset, customizing data pipeline, customizing vid model, customizing mot model, customizing sot model, customizing runtime settings and useful tools.


We appreciate all contributions to improve MMTracking. Please refer to for the contributing guideline.


MMTracking is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new video perception methods.


If you find this project useful in your research, please consider cite:

    title={{MMTracking: OpenMMLab} video perception toolbox and benchmark},
    author={MMTracking Contributors},
    howpublished = {\url{}},

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM installs OpenMMLab packages.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition and understanding toolbox.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning Toolbox and Benchmark.
  • MMRazor: OpenMMLab Model Compression Toolbox and Benchmark.
  • MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMGeneration: OpenMMLab Generative Model toolbox and benchmark.
  • MMDeploy: OpenMMlab deep learning model deployment toolset.


OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

License:Apache License 2.0


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