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!!
Three versions (full dataset, RGB split and mask split) are available. Please refer to our project page.

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.

How to evaluate

Note: The dataset should be extracted into the same folder.

RGB-T tracker evaluation

Modify the variable "basePath" in GenerateMat_ST.m and GenerateMat_LT.m and move your results into "BB_results" folder
run GenerateMat_ST.m and GenerateMat_LT.m to generate the report files for short-term and long-term tracking
If only overall performance is needed, directly run plot_ST.m and plot_LT.m to generate the MSR and MPR curves.
If both overall and attribute-based performance are needed, change the "attrDisplays" and run plot_ST.m and plot_LT.m to generate the MSR and MPR curves.

RGB tracker evaluation

Modify the variable "basePath" in GenerateMat_ST_RGB_only.m and GenerateMat_LT_RGB_only.m and move your results into "BB_results_RGB" folder
run GenerateMat_ST_RGB_only.m and GenerateMat_LT_RGB_only.m to generate the report files for short-term and long-term tracking
If only overall performance is needed, directly run plot_ST_RGB_only.m and plot_LT_RGB_only.m to generate the MSR and MPR curves.
If both overall and attribute-based performance are needed, change the "attrDisplays" and run plot_ST_RGB_only.m and plot_LT_RGB_only.m to generate the MSR and MPR curves.

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.

hitersyw/DUT-VTUAV