Awesome

End-to-End Feature Decontaminated Network for UAV Tracking

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

Abstract

Object feature pollution is one of the burning issues in UAV tracking, which is commonly caused by occlusion, fast motion, and illumination variation. Due to the contaminated information in the polluted object features, most trackers fail to precisely estimate the location and scale of the object. To address the feature pollution issue, this work proposes an efficient and effective adaptive feature resampling tracker, i.e., AFRT. AFRT mainly includes two stages: an adaptive downsampling network which can reduce the interference information of the feature pollution and a super-resolution upsampling network, applying Transformer to restore the object scale information. Specifically, the adaptive downsampling network strengthens the expression of the object location information, with a feature enhancement downsampling (FED) module. In order to achieve better training effect, a novel pooling distance loss function is designed to help FED module focus on the critical regions with the object information. Thereby, the features downsampled can be validly exploited to determine the location of the object. Subsequently, the super-resolution upsampling network raises the scale information in the features with a low-to-high (LTH) Transformer encoder. Exhaustive experiments on three well-known benchmarks validate the effectiveness of AFRT, especially on the sequences with feature pollution. In addition, real-world tests show the efficiency of AFRT with 31.4 frames per second. The code and demo videos are available at: https://github.com/vision4robotics/FDNT.

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: AFRTmodel(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/AFRTmodel.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

Train a model

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

   cd tools
   python train.py

4. Evaluation

We provide the tracking results(code: 6q8m) of 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 'AFRTmodel'   # tracker_name

5. Contact

If you have any questions, please contact me.

Haobo Zuo

Email: 1951684@tongji.edu.cn

Demo Video

Acknowledgement

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