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 (coming soon) | NAT2021 benchmark
Testing UDAT
1. Preprocessing
Before training, we need to preprocess the unlabelled training data to generate training pairs.
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Download the proposed NAT2021-train set
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Customize the directory of the train set in
lowlight_enhancement.py
and enhance the nighttime sequencescd preprocessing/ python lowlight_enhancement.py # enhanced sequences will be saved at '/YOUR/PATH/NAT2021/train/data_seq_enhanced/'
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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
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Generate pseudo annotations from candidate boxes using dynamic programming
python gen_seq_bboxes.py # pseudo box sequences will be saved at 'pseudo_anno/'
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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.
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Apart from above target domain dataset NAT2021, you need to download and prepare source domain datasets VID and GOT-10K.
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Download the pre-trained daytime model (SiamCAR/SiamBAN) and place it at
UDAT/tools/snapshot
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Start training
cd UDAT/CAR export PYTHONPATH=$PWD python tools/train.py
3. Test
Take UDAT-CAR for instance.
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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 [email protected] or Changhong Fu at [email protected].