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Adaptiave Colour-Depth Aware Attention for RGB-D Object Tracking

The official PyTorch implementation of the CDAAT tracker:

Framework

The tracking pipeline is illustrated as:

The Colour-Depth Aware Attention Module is as:

<img src=cdaam.png width="660" height="330" >

Usage

Install the environment using Anaconda

conda create -n cdaat python=3.7
conda activate cdaat
sudo apt-get install libturbojpeg
bash install.sh

The training process contains two stages:

Make sure you have prepared the trained model. You can train it by yourself or download from Google Drive. Edit ./lib/test/evaluation/local.py and ./lib/test/parameter/cdaatrack.py to set the test set path and the pretrained model path, then run

python ./tracking/test.py

You can download the raw results from Google Drive, and evaluate the raw results using the VOT toolkit.

Acknowledgement

Our idea is implemented base on the following projects. We really appreciate their wonderful open-source work!

Contact

If you have any questions or concerns, please feel free to contact us.