Quick Start

The soure code of the paper "Material-Guided Multiview Fusion Network for Hyperspectral Object Tracking".

1. Environment Setting

The environment configuration follows https://github.com/fzh0917/STMTrack.

Prepare Anaconda, CUDA and the corresponding toolkits. CUDA version required: 10.0+;

Create a new conda environment and activate it.

  conda create -n MMFNet python=3.7 -y
  conda activate MMFNet

Install pytorch and torchvision.

  conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
  # pytorch v1.5.0, v1.6.0, or higher should also be OK.

Install other required packages.

  pip install -r requirements.txt

2. Dataset

• The hyperspectral videos datasets are from "https://www.hsitracking.com/".
• The Material View is generated by the code from the paper "Material Based Object Tracking in Hyperspectral Videos".

3. Train

(a) Download pretrained model in

- https://pan.baidu.com/s/1vBmGFoQ4MRTUeLE3o7pteg 
- Access code: 1234    

(b) Change the path of training data in videoanalyst/evaluation/.

(c) Run: train.sh

4. Test

(a) Download testing model in

- https://pan.baidu.com/s/15YdmJRvagPzKcUNWBloiHA 
- Access code: 1234  

(b) Put the testing model in snapshots/stmtrack-googlenet-got-train;

(c) Run: test.sh

5. Cite

  @ARTICLE{10438474,
  author={Li, Zhuanfeng and Xiong, Fengchao and Zhou, Jun and Lu, Jianfeng and Zhao, Zhuang and Qian, Yuntao},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Material-Guided Multiview Fusion Network for Hyperspectral Object Tracking}, 
  year={2024},
  volume={62},
  number={},
  pages={1-15},
  keywords={Feature extraction;Hyperspectral imaging;Target tracking;Videos;Object tracking;Visualization;Spatial resolution;Hyperspectral object tracking;hyperspectral unmixing;multihead attention;multiview fusion},
  doi={10.1109/TGRS.2024.3366536}}

6. Concat

• lizhuanfeng@njust.edu.cn;
• If you have any questions, just contact me.