Adversarial Blur-Deblur Network for Robust UAV Tracking

Haobo Zuo, Changhong Fu, Sihang Li, Kunhan Lu, Yiming Li, Chen Feng

About Code

1. Environment setup

This code has been tested on Ubuntu 18.04, Python 3.8.3, Pytorch 0.7.0/1.6.0, CUDA 10.2. Please install related libraries before running this code:

  pip install -r requirements.txt

2. Test

Download pretrained model: ABDNmodel(code: huat) and put it into tools/snapshot directory.

Download testing datasets and put them into test_dataset directory. If you want to test the tracker on a new dataset, please refer to pysot-toolkit to set test_dataset.

   python test.py 
        --dataset UAV123                #dataset_name
        --snapshot snapshot/ABDNmodel.pth  # tracker_name

The testing result will be saved in the results/dataset_name/tracker_name directory.

3. Train

Prepare training datasets

Download the datasets：

VID

COCO

GOT-10K

LaSOT

VisDrone-SOT2019

Train a model

To train the ABDN model, run train.py with the desired configs:

   cd tools
   python train.py

4. Evaluation

We provide the tracking results(code: 6q8m) of DTB70, UAV123@10fps, UAV123, and UAVTrack112. If you want to evaluate the tracker, please put those results into results directory.

    python eval.py 	                          \
         --tracker_path ./results          \ # result path
         --dataset UAV123                  \ # dataset_name
         --tracker_prefix 'ABDNmodel'   # tracker_name

5. Demo

The different blurred images with different blur degrees and the example of deblurred images are displayed below.

6. Contact

If you have any questions, please contact me.

Haobo Zuo

Email: [email protected]

Acknowledgement

The code is implemented based on pysot. We would like to express our sincere thanks to the contributors.