AD-SiamRPN

1. Environment setup

This code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 0.4.1/1.2.0, CUDA 9.0. Please install related libraries before running this code:

pip install -r requirements.txt

2. Test

Dataset		ADSiamRPN	BAENet	MFIHVT	MHT	DeepHKCF	BS-SiamRPN	SiamRPN++	DaSiamRPN
HOT2022	Success	57.5	61.6	60.1	58.4	38.5	53.3	52.9	55.8
	Precision	86.1	87.6	89.1	87.6	73.7	84.5	83.4	83.1

Download the pretrained model:
model code: bm1e

and put them into models directory.

Download the test result:

hot2022_result code: 4taf

3. Acknowledgement

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

4. Cite

If you use ADSiamRPN in your work please cite our papers:

	@article{wang2023ad,
	  title={AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos},
	  author={Wang, Shiqing and Qian, Kun and Shen, Jianlu and Ma, Hongyu and Chen, Peng},
	  journal={Remote Sensing},
	  volume={15},
	  number={7},
  pages={1731},
	  year={2023},
	  publisher={MDPI}
	}

   @inproceedings{wang2022bs,
     title={BS-SiamRPN: Hyperspectral video tracking based on band selection and the Siamese region proposal network},
     author={Wang, ShiQing and Qian, Kun and Chen, Peng},
     booktitle={2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
     pages={1--8},
  year={2022},
     organization={IEEE}
   }