Awesome

SGDViT: Saliency-Guided Dynamic vision Transformer for UAV tracking

Liangliang Yao, Changhong Fu, Sihang Li, Guangze Zheng and Junjie Ye

Abstract

Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the frequent maneuvering flight and viewpoint change are prone to cause nerve-wracking challenges, e.g., aspect ratio change and scale variation. The cross-correlation operation’s weak ability to mine perceptual similarity and easy introduction of background information become more apparent when confronted with these challenges. To address these issues, this work proposes a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. Specifically, a UAV task-oriented object saliency mining network is designed to refine the perceptual similarity indicated by cross-correlation operation, distinguishing the foreground and background preliminarily. Furthermore, an innovative saliency adaption embedding operation is developed to generate dynamic tokens based on the initial saliency, reducing the computational complexity of the Transformer structure. On this bases, a lightweight saliency filtering Transformer is implemented to refine the saliency information and increase attention to the appearance information. Comprehensive evaluations on three authoritative UAV tracking benchmarks and real-world tests have proven the effectiveness and robustness of the proposed method.

This figure shows the workflow of our tracker.

Demo

• 📹 Demo of real-world SGDViT tests.
• Refer to Test1 and Test2 on Bilibili for more real-world tests.

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: result(code: x7jh) 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 ./tools/test.py                                
	--dataset UAV10fps                 #dataset_name
	--snapshot snapshot/result.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
• YOUTUBEBB (code: t7j8)
• COCO
• GOT-10K

Note: train_dataset/dataset_name/readme.md has listed detailed operations about how to generate training datasets.

Train a model

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

python ./tools/train.py

4. Evaluation

We provide the tracking results (code: l9qy) of UAV123@10fps, DTB70, 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 UAV10                   \ # dataset_name
	--tracker_prefix 'result'   # tracker_name

5. Contact

If you have any questions, please contact me.

Liangliang Yao

Email: 1951018@tongji.edu.cn

Acknowledgement

The code is implemented based on pysot, HiFT, and Swin-T. We would like to express our sincere thanks to the contributors.