HyperTrack：A Unified Framework for Hyperspectral Object Tracking

Official implementation of HyperTrack, including models and training&testing codes.

Introduction

A new unified hyperspectral tracking framework (e.g. VIS, NIR, and RedNIR Tracking).

HyperTrack has high performance on hyperspectal tracking tasks.
A single model cross three kinds of hyperspectral images.

Results

On HOT2023 tracking benchmarks

On HOT2022 tracking benchmark

Usage

Installation

Create and activate a conda environment:

conda create -n hypertrack python=3.7
conda activate hypertrack

Install the required packages:

bash install_hypertrack.sh

Data Preparation

Put the training datasets in ./data/. It should look like:

$<PATH_of_HyperTrack>
-- data
    -- Test
        |-- VIS
        |-- NIR
        |-- RedNIR
        ...
    -- Train
        |-- VIS
        |-- NIR
        |-- RedNIR
        ...

Path Setting

Run the following command to set paths:

cd <PATH_of_HyperTrack>
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./output

You can also modify paths by these two files:

./lib/train/admin/local.py  # paths for training
./lib/test/evaluation/local.py  # paths for testing

Training

Download the pretrained foundation model (Mixformer) and put it under ./pretrained_networks/.

python run_training.py

You can train models with various modalities and variants by modifying run_train.py.

Testing

HOT2022/HOT2023

python test.py

You can test models with various modalities and variants by modifying test.py.

Acknowledgment

This repo is based on Mixformer which is an excellent work.
We thank for the PyTracking library, which helps us to quickly implement our ideas.

supertyd/HyperTrack