This work has been accepted and published by Artificial Intelligence Review (JCR Q1, IF = 12).
This code library gives our experimental results and most of the publicly available Siamese trackers.
The trackers are in folder experiments and the results are in results.
Paper link: https://link.springer.com/article/10.1007/s10462-023-10558-5
View-only link: https://rdcu.be/dhWgD
If you want to use our code libary, experimental results, and related contents, please cite our paper using the format as follows:
@article{Fu2023SiameseOT,
title={{Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis}},
author={Fu, Changhong and Lu, Kunhan and Zheng, Guangze and Ye, Junjie and Cao, Ziang and Li, Bowen and Lu, Geng},
journal={Artificial Intelligence Review},
volume={56},
number={Suppl 1},
pages={1417-1477},
year={2023},
doi={10.1007/s10462-023-10558-5}
}
The trackers are tested on the following platform.
- Ubuntu 18.04
- 8-core Carmel ARM v8.2 64-bit CPU
- 512-core Volta GPU
- 32G RAM
- CUDA 10.2
- Python 3.6.3
- Pytorch 0.7.0/1.6.0
All the Siamese trackers' results are obtained using an NVIDIA Jetson AGX Xavier.
Here shows some of the tracking results of 19 SOTA Siamese trackers.
Comparison of the performance under all the six authoritative UAV tracking benchmarks.
The average performance comparison of the five real-time Siamese trackers under all the six authoritative UAV tracking benchmarks.
This code has been tested on an NVIDIA Jetson AGX Xavier with Ubuntu 18.04, Python 3.6.3, Pytorch 0.7.0/1.6.0, CUDA 10.2.
Please install related libraries before running this code:
pip install pyyaml yacs tqdm colorama matplotlib cython tensorboardX easydictDownload pretrained models form the links in experiments directory or download pretrained models from official code site and put them into experiments 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.
The papers whose benchmarks are used in the experimental evaluations are listed here.
Paper: A Benchmark and Simulator for UAV Tracking
Paper site: https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_27 .
Code and benchmark site: https://cemse.kaust.edu.sa/ivul/uav123 .
Paper: Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models
Paper site: https://dl.acm.org/doi/10.5555/3298023.3298169 .
Code and benchmark site: https://github.com/flyers/drone-tracking .
Paper: The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Paper site: https://link.springer.com/article/10.1007/s11263-019-01266-1 .
Code and benchmark site: https://sites.google.com/site/daviddo0323/projects/uavdt .
Paper: VisDrone-SOT2020: The Vision Meets Drone Single Object Tracking Challenge Results
Paper site: https://link.springer.com/chapter/10.1007/978-3-030-66823-5_44 .
Code and benchmark site: http://aiskyeye.com/ .
Paper: Onboard Real-Time Aerial Tracking With Efficient Siamese Anchor Proposal Network
Paper site: https://ieeexplore.ieee.org/abstract/document/9477413 .
Code and benchmark site: https://github.com/vision4robotics/SiamAPN .
Paper: All-Day Object Tracking for Unmanned Aerial Vehicle
Paper site: https://ieeexplore.ieee.org/document/9744417 .
Code and benchmark site: https://github.com/vision4robotics/ADTrack_v2 .
Paper: Tracker Meets Night: A Transformer Enhancer for UAV Tracking
Paper site: https://ieeexplore.ieee.org/document/9696362 .
Code and benchmark site: https://github.com/vision4robotics/SCT .
Paper: Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Paper site: https://ieeexplore.ieee.org/document/9879981 .
Code and benchmark site: https://vision4robotics.github.io/NAT2021/ .
Use the corresponding 'tools/test_<tracker_name>.py' to test the performance of the tracker. Take the test of SiamAPN as an example:
python tools/test_siamapn.py \
--dataset UAVTrack112 \ # dataset_name
--datasetpath ./test_dataset \ # dataset_path
--config ./experiments/SiamAPN/config.yaml \ # tracker_config
--snapshot ./experiments/SiamAPN/model.pth \ # tracker_model
--trackername SiamAPN # tracker_name
The testing result will be saved in the results/<dataset_name>/<tracker_name> directory.
The settings required by different trackers will be different. For details, please refer to the examples in 'tools/test.sh'
Similar to Option 1, a more convenient way of testing is provided using 'tools/test.py' to test all the trackers.
python tools/test.py \
--dataset UAVTrack112 \ # dataset_name
--datasetpath ./test_dataset \ # dataset_path
--config ./experiments/SiamAPN/config.yaml \ # tracker_config
--snapshot ./experiments/SiamAPN/model.pth \ # tracker_model
--trackername SiamAPN # tracker_name
If you want to evaluate the trackers mentioned above, please put those results into results directory as results/<dataset_name>/<tracker_name>.
python tools/eval.py \
--dataset UAVTrack112 \ # dataset_name
--datasetpath path/of/your/dataset \ # dataset_path
--tracker_path ./results \ # result_path
--tracker_prefix 'SiamAPN' # tracker_name
If you have any questions, please contact me.
Kunhan Lu
Email: lukunhan@tongji.edu.cn .
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The code is implemented based on pysot. We would like to express our sincere thanks to the contributors.
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We would like to thank Jilin Zhao, Kunhui Chen, Haobo Zuo, and Sihang Li for their help in building this code library.
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We also thank the contribution of Matthias Muller, Siyi Li, Dawei Du, Heng Fan et al. for their previous work of the benchmarks UAV123@10fps, UAV20L, DTB70, UAVDT, and VisDrone-SOT2020-test.