DPTracker

DPTracker: DYNAMIC PROMPTER FOR RGB-D Tracking

Architecture & Module Details

Overall Architecture

Core Modules

Dynamic Prompter (DP)

Modality Effectiveness Predictor (MEP)

Trained Checkpoints

We provide trained checkpoints (https://drive.google.com/open?id=1HjNX06R-1JtUzgW3ZJ9QbDqgJunzk4Nc&usp=drive_fs)

Usage

Installation

Create and activate a conda environment:

conda create -n dptracker python=3.7
conda activate dptracker

Install the required packages:

bash install_dptracker.sh

Data Preparation

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

$<PATH_of_DPTracker>
-- data
    -- DepthTrackTraining
        |-- adapter02_indoor
        |-- bag03_indoor
        |-- bag04_indoor
        ...

Training

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

bash train_dptracker.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

Acknowledgment

This repo is based on ViPT which is an excellent work.