[ECCV'22 Oral] Towards Grand Unification of Object Tracking

Unicorn 🦄 : Towards Grand Unification of Object Tracking

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

Highlight

• 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.

Introduction

• 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)

Demo

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

Results

SOT

MOT (MOT17)

MOT (BDD100K)

VOS

MOTS (MOTS Challenge)

MOTS (BDD100K MOTS)

Getting started

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.

Citing Unicorn

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}
}

Acknowledgments

• 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.