Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking

Changhong Fu, Mutian Cai, Sihang Li, Kunhan Lu, Haobo Zuo and Chongjun Liu

Abstract

Object tracking is crucial for the autonomous navigation of unmanned aerial vehicles (UAVs) and has broad application in robotic automation fields. However, reliable aerial tracking remains a challenging task due to various difficulties like frequent occlusion and aspect ratio change. Additionally, most of the existing work mainly focus on explicit information to improve tracking performance, ignoring potential connections between frames. To solve the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i.e., ClimRT. Specifically, a new efficient continuity-aware latent interframe information mining network (ClimNet) is proposed for UAV tracking, which can generate highly-effective latent frames between two adjacent frames. Besides, a novel location-continuity Transformer (LCT) is designed to fully explore continuity-aware spatial-temporal information, thereby markedly enhancing UAV tracking. Extensive qualitative and quantitative experiments on three authoritative aerial benchmarks have strongly validated the robustness and reliability of ClimRT in UAV Tracking. Furthermore, real-world tests on the typical aerial platform have proven its practicability and effectiveness.

This figure shows the workflow of our tracker.

Demo

• 📹 Demo of real-world ClimRT tests.
• Refer to ClimRT-RWT on Bilibili for the demo.

About Code

1. Environment setup

The ClimNet has been tested on Ubuntu 18.04, Python 3.7.4, numpy==1.19.2 , Pytorch 1.5.0, torchvision==0.6.0, cudatoolkit==10.1.

The ClimRT 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 requirement.txt

2. Test

Download pretrained model for backbone: alexnet-bn.pth(code: 2lr3) and put it into ./pretrained_models directory. Download pretrained model for ClimNet: model_best.pth(code: mc8c) and put it into ./pretrained_models directory. Download pretrained model for ClimRT: t_best.pth(code: lswe) and put it into tools/snapshot directory.

Download testing datasets and put them into 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/t_best.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：

• Vimeo-90K
• VID
• 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 ClimNet model, run main.py with the desired configs:

python main.py --batch_size 32 --test_batch_size 32 --dataset vimeo90K_septuplet --loss 1*L1 --max_epoch 100 --lr 0.002 --data_root /train_dataset/vimeo_triplet --n_outputs 1 

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

cd tools
python train.py

4. Evaluation

We provide the tracking results (code: 296s) of 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 't_best'   # tracker_name

5. Contact

If you have any questions, please contact me.

Mutian Cai

Email: [email protected]

Acknowledgement

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