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MasterBin-IIAU/Unicorn: [ECCV'22 Oral] Towards Grand Unification of Object Tracking

This repository is the project page for the paper Towards Grand Unification of Object Tracking

• Unicorn is accepted to ECCV 2022 as an oral presentation!
• Unicorn first demonstrates grand unification for four object-tracking tasks.
• Unicorn achieves strong performance in eight tracking benchmarks.

• The object tracking field mainly consists of four sub-tasks: Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Segmentation (VOS), and Multi-Object Tracking and Segmentation (MOTS). Most previous approaches are developed for only one of or part of the sub-tasks.

• For the first time, Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four tracking tasks. Besides, Unicorn puts forwards new state-of-the-art performance on many challenging tracking benchmarks using the same model parameters.

This repository supports the following tasks:

Image-level

• Object Detection
• Instance Segmentation

Video-level

• Single Object Tracking (SOT)
• Multiple Object Tracking (MOT)
• Video Object Segmentation (VOS)
• Multi-Object Tracking and Segmentation (MOTS)

Unicorn conquers four tracking tasks (SOT, MOT, VOS, MOTS) using the same network with the same parameters.

1. Installation: Please refer to install.md for more details.
2. Data preparation: Please refer to data.md for more details.
3. Training: Please refer to train.md for more details.
4. Testing: Please refer to test.md for more details.
5. Model zoo: Please refer to model_zoo.md for more details.

If you find Unicorn useful in your research, please consider citing:

@inproceedings{unicorn,
  title={Towards Grand Unification of Object Tracking},
  author={Yan, Bin and Jiang, Yi and Sun, Peize and Wang, Dong and Yuan, Zehuan and Luo, Ping and Lu, Huchuan},
  booktitle={ECCV},
  year={2022}
}

• Thanks YOLOX and CondInst for providing strong baseline for object detection and instance segmentation.
• Thanks STARK and PyTracking for providing useful inference and evaluation toolkits for SOT and VOS.
• Thanks ByteTrack, QDTrack and PCAN for providing useful data-processing scripts and evalution codes for MOT and MOTS.

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