DUT-VTUAV

We release a large-scale benchmark for Visble-thermal UAV Tracking.

News

The dataset is available on VTUAV.
Our paper is accepted by CVPR2022!!

Main Feature

Large-scale: We collected nearly 1.7 million well-aligned RGB-T image pairs with 500 sequences for unveiling the power of RGB-T tracking(the largest RGB-T tracking benchmark so far).
High-diversity:13 sub-classes and 15 scenes cross 2 cities.
Multi-task evaluation: Our benchmark is designed for evaluating both short-term tracking, long-term tracking and tracking with segmentation.
Hierarchical attribute annotation: Sequence-level attribute annotation for 13 typical challenges. Additionally, we provide frame-level attribute for training challenge-aware trackers.

Download from google Drive (python and gdown are required)

import gdown
url = "https://drive.google.com/drive/folders/1GwYNPcrkUM-gVDAObxNqERi_2Db7okjP?usp=sharing"
gdown.download_folder(url, quiet=False, use_cookies=False)

Results for RGB trackers on short-term subset

Results for RGB trackers on long-term subset

Results for RGB-T trackers on short-term subset

	MSR	MPR
DAFNet	45.8	62.0
ADRNet	46.6	62.2
FSRPN	54.4	65.3
mfDiMP	55.4	67.3
HMFT	62.7	75.8

Results for RGB-T trackers on long-term subset

	MSR	MPR
ADRNet	17.5	23.5
DAFNet	18.8	25.3
FSRPN	27.2	31.5
mfDiMP	31.4	36.6
HMFT	35.5	41.4
HMFT_LT	46.1	53.6

Evaluation toolkit & attribute annotation

The sequence-level attribute annotation can be found in BaiduDisk(code:h24u) and GoogleDrive.
The evaluation toolkit can be found in BaiduDisk(code:99j9) and GoogleDrive.

Reference

If you find this benchmark useful, please cite

@InProceedings{Zhang_CVPR22_VTUAV,
author = {Zhang Pengyu and Jie Zhao and Dong Wang and Huchuan Lu and Xiang Ruan},
title = {Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2022}
}

Contact

If you have any questions, feel free to contact me.

wangdongdut/DUT-VTUAV