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Visual Prompt Multi-Modal Tracking [CVPR2023]

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

Models & Raw Results (Google Driver) Models & Raw Results (Baidu Driver: vipt)

:fire::fire::fire: This work proposes ViPT, a new prompt-tuning framework for multi-modal tracking.

• Tracking in RGB + Depth scenarios:

  <center><img width="75%" alt="" src="assets/rgbd_demo.gif"/></center>

• Tracking in RGB + Thermal scenarios:

  <center><img width="75%" alt="" src="assets/rgbt_demo.gif"/></center>

• Tracking in RGB + Event scenarios:

  <center><img width="75%" alt="" src="assets/rgbe_demo.gif"/></center>

News

[Mar 20, 2023]

• We release codes, models and raw results.
  Thanks for your star :stuck_out_tongue_closed_eyes::stuck_out_tongue_closed_eyes::stuck_out_tongue_closed_eyes:.

[Feb 28, 2023]

• ViPT is accepted to CVPR2023.

Introduction

• :fire: A new unified visual prompt multi-modal tracking framework (e.g. RGB-D, RGB-T, and RGB-E Tracking).

• ViPT has high performance on multiple multi-modal tracking tasks.

• ViPT is with high parameter-efficient tuning, containing only 0.84M trainable parameters (<1%).

• We expect ViPT can attract more attention to prompt learning :fire: for further research of multi-modal tracking.

Results

On RGB-D tracking benchmarks

On RGB-T tracking benchmarks

On RGB-E tracking benchmark

Usage

Installation

Create and activate a conda environment:

conda create -n vipt python=3.7
conda activate vipt

Install the required packages:

bash install_vipt.sh

Data Preparation

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

$<PATH_of_ViPT>
-- data
    -- DepthTrackTraining
        |-- adapter02_indoor
        |-- bag03_indoor
        |-- bag04_indoor
        ...
    -- LasHeR/train/trainingset
        |-- 1boygo
        |-- 1handsth
        ...
    -- VisEvent/train
        |-- 00142_tank_outdoor2
        |-- 00143_tank_outdoor2
        ...
        |-- trainlist.txt

Path Setting

Run the following command to set paths:

cd <PATH_of_ViPT>
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

Dowmload the pretrained foundation model (OSTrack) and put it under ./pretrained/.

bash train_vipt.sh

You can train models with various modalities and variants by modifying train_vipt.sh.

Testing

For RGB-D benchmarks

[DepthTrack Test set & VOT22_RGBD]
These two benchmarks are evaluated using VOT-toolkit.
You need to put the DepthTrack test set to./Depthtrack_workspace/ and name it 'sequences'.
You need to download the corresponding test sequences at./vot22_RGBD_workspace/.

bash eval_rgbd.sh

For RGB-T benchmarks

[LasHeR & RGBT234]
Modify the <DATASET_PATH> and <SAVE_PATH> in./RGBT_workspace/test_rgbt_mgpus.py, then run:

bash eval_rgbt.sh

We refer you to LasHeR Toolkit for LasHeR evaluation, and refer you to MPR_MSR_Evaluation for RGBT234 evaluation.

For RGB-E benchmark

[VisEvent]
Modify the <DATASET_PATH> and <SAVE_PATH> in./RGBE_workspace/test_rgbe_mgpus.py, then run:

bash eval_rgbe.sh

We refer you to VisEvent_SOT_Benchmark for evaluation.

Bixtex

If you find ViPT is helpful for your research, please consider citing:

@inproceedings{ViPT,
  title={Visual Prompt Multi-Modal Tracking},
  author={Jiawen, Zhu and Simiao, lai and Xin, Chen and Wang, Dong and Lu, Huchuan},
  booktitle={CVPR},
  year={2023}
}

Acknowledgment

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

Contact

If you have any question, feel free to email jiawen@mail.dlut.edu.cn. ^_^