DeconNet: End-to-End Decontaminated Network for Vision-Based Aerial Tracking

Haobo Zuo, Changhong Fu, Sihang Li, Junjie Ye, and Guangze Zheng

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: DeconNetmodel(code: ugr9) 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/DeconNetmodel.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：

Train a model

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

   cd tools
   python train.py

4. Evaluation

We provide the tracking results(code: rk4j) of DTB70, UAV123@10fps, UAV123, and UAVTrack112_L. 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 'DeconNetmodel'   # tracker_name

5. Contact

If you have any questions, please contact me.

Haobo Zuo

Email: 1951684@tongji.edu.cn

Acknowledgement

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

vision4robotics/DeconNet