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.txtDownload pretrained model 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 test.py \
--trackername SiamAPN \ # tracker_name
--dataset UAV10fps \ # dataset_name
--snapshot snapshot/general_model.pth # model_path
The testing result will be saved in the results/dataset_name/tracker_name directory.
Download the datasets：
Note: train_dataset/dataset_name/readme.md has listed detailed operations about how to generate training datasets.
To train the SiamAPN model, run train.py with the desired configs:
cd tools
python train_apn.py The pre-trained model can be found at (epoch=37) : general_model(code:w3u5)
We provide the tracking results_v1 (code: s3p1) of UAV123@10fps, UAV20L, and VisDrone2018-SOT-test. Besides, the tracking results_v2 (code: j4t5) of UAV123@10fps, UAV20L, VisDrone2018-SOT-test and UAVTrack112 are also provided.
The pre-trained model can be found at (epoch=25): general_model(code:j29k)
We provide the tracking results (code: xb41) of UAV123@10fps, UAV20L.
**Note:**The pre-trained model of SiamAPN and SiamAPN++ can also be found at googledriver
If you want to evaluate the tracker mentioned above, please put those results into results directory.
python eval.py \
--tracker_path ./results \ # result path
--dataset UAV10fps \ # dataset_name
--tracker_prefix 'general_model' # tracker_name
UAVTrack112 benchmark is created from images captured during the real-world tests. It can be downloaded at UAVTrack112 (code: xb41). If you want to use this UAVTracking benchmark, please cite the paper below. More detail of the benchmark are available in here.
@INPROCEEDINGS{fu2021siamese,
author={Fu, Changhong and Cao, Ziang and Li, Yiming and Ye, Junjie and Feng, Chen},
booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
title={{Siamese Anchor Proposal Network for High-Speed Aerial Tracking}},
year={2021},
volume={},
number={},
pages={1-7}
}
@INPROCEEDINGS{fu2021tgrs,
author={Fu, Changhong and Cao, Ziang and Li, Yiming and Ye, Junjie and Feng, Chen},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={{Onboard Real-Time Aerial Tracking with Efficient Siamese Anchor Proposal Network}},
year={2021},
volume={},
number={},
pages={1-13}
}
If you have any questions, please contact me.
Ziang Cao
Email: [email protected]
The code is implemented based on pysot. We would like to express our sincere thanks to the contributors.
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