Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022)

Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, and Guang Chen. Unsupervised Domain Adaptation for Nighttime Aerial Tracking. In CVPR, pages 1-10, 2022.

Overview

UDAT is an unsupervised domain adaptation framework for visual object tracking. This repo contains its Python implementation.

Paper | NAT2021 benchmark

Testing UDAT

1. Preprocessing

Before training, we need to preprocess the unlabelled training data to generate training pairs.

Download the proposed NAT2021-train set

Customize the directory of the train set in lowlight_enhancement.py and enhance the nighttime sequences

cd preprocessing/
python lowlight_enhancement.py # enhanced sequences will be saved at '/YOUR/PATH/NAT2021/train/data_seq_enhanced/'

Download the video saliency detection model here and place it at preprocessing/models/checkpoints/.

Predict salient objects and obtain candidate boxes

python inference.py # candidate boxes will be saved at 'coarse_boxes/' as .npy

Generate pseudo annotations from candidate boxes using dynamic programming

python gen_seq_bboxes.py # pseudo box sequences will be saved at 'pseudo_anno/'

Generate cropped training patches and a JSON file for training
```
python par_crop.py
python gen_json.py
```

2. Train

Take UDAT-CAR for instance.

Apart from above target domain dataset NAT2021, you need to download and prepare source domain datasets VID and GOT-10K.
Download the pre-trained daytime model (SiamCAR/SiamBAN) and place it at UDAT/tools/snapshot.

Start training

cd UDAT/CAR
export PYTHONPATH=$PWD
python tools/train.py

3. Test

Take UDAT-CAR for instance.

For quick test, you can download our trained model for UDAT-CAR (or UDAT-BAN) and place it at UDAT/CAR/experiments/udatcar_r50_l234.
Start testing
```
python tools/test.py --dataset NAT
```

4. Eval

Start evaluating
```
python tools/eval.py --dataset NAT
```

Demo

Reference

@Inproceedings{Ye2022CVPR,

title={{Unsupervised Domain Adaptation for Nighttime Aerial Tracking}},

author={Ye, Junjie and Fu, Changhong and Zheng, Guangze and Paudel, Danda Pani and Chen, Guang},

booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

year={2022},

pages={1-10}

}

Acknowledgments

We sincerely thank the contribution of following repos: SiamCAR, SiamBAN, DCFNet, DCE, and USOT.

Contact

If you have any questions, please contact Junjie Ye at ye.jun.jie@tongji.edu.cn or Changhong Fu at changhongfu@tongji.edu.cn.

ssssss-ai/UDAT