This is the source code for this paper called "SiamBAG: Band Attention Grouping-based Siamese Object Tracking Network for Hyperspectral Videos"

Video tracking results in worker scenario:

Note that: in this video, the red bounding box is SiamBAG tracker, the blue one is ground truth.

GIF tracking results in some scenarios:

Note that: in these scenarios, the black bounding box is SiamBAG tracker, the blue one is ground truth, and the red one is BAENet tracker.

Some important environments for SiamBAG.

1. Python 3.9
2. PyTorch 1.13.0
3. CUDA 11.6

Please refer to 'SiamBAG-Installation_Environment.txt' file for more detailed.

Paper Download:

• IEEE Xplore

Pretrained model Download:
You can download the pretrained model.pth from Google Drive or Baidu Yun with extract password [gloh], and put the file under pretrained/siamrpn.

If these codes are helpful for you, please cite this paper:

BibTex Format:

@ARTICLE{10149343,
author={Li, Wei and Hou, Zengfu and Zhou, Jun and Tao, Ran},
journal={IEEE Transactions on Geoscience and Remote Sensing}, 
title={SiamBAG: Band Attention Grouping-based Siamese Object Tracking Network for Hyperspectral Videos}, 
year={2023},
volume={61},
number={},
pages={1-12},
doi={10.1109/TGRS.2023.3285802}}

Plain Text Format:

W. Li, Z. Hou, J. Zhou and R. Tao, "SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023, Art no. 5514712, doi: 10.1109/TGRS.2023.3285802.