Haoyang Li, Changhong Fu, Guangze Zheng, Sihang Li, and Junjie Ye. Multi-View Domain Adaptation for Nighttime Aerial Tracking.

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

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

1. Download the proposed NAT2021-train set

2. 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/'

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

4. Predict salient objects and obtain candidate boxes

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

5. Generate pseudo annotations from candidate boxes using dynamic programming

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

6. Generate cropped training patches and a JSON file for training

   python par_crop.py
   python gen_json.py

Take MVDANT for instance.

1. Apart from above target domain dataset NAT2021, you need to download and prepare source domain datasets VID and GOT-10K.

2. Download the pre-trained daytime model (SiamCAR/SiamBAN) and place it at UDAT/tools/snapshot.

3. Start training

   cd MVADNT
   export PYTHONPATH=$PWD
   python tools/train_MVADNT.py

Take MVADNT for instance.

1. For quick test, you can download our trained model for MVADNT and place it at MVADNT/CAR/experiments/udatcar_r50_l234.

2. Start testing

   python tools/test.py --dataset NAT 
   python tools/test.py --dataset UAVDark70

1. Start evaluating

   python tools/eval.py --dataset NAT
   python tools/eval.py --dataset UAVDark70