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DATA.md

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Dataset preparation

If you want to reproduce the results in the paper for benchmark evaluation or training, you will need to setup datasets.

ParallelDomain (PD)

This is the synthetic dataset proposed in PermaTrack. You can download the images together with annotations under this link. After downloading, copy the contents into $RAM_ROOT/data/pd.

KITTI Tracking

We use KITTI Tracking to train and evaluate the system in the real world. Following prior work, we will only use the training set (and create a validation set from it) for developing this project.

  • Download images, and annotations from KITTI Tracking website and unzip. Place or symlink the data as below:

    ${RAM_ROOT}
|-- data
`-- |-- kitti_tracking
    `-- |-- data_tracking_image_2
        |   |-- training
        |   |-- |-- image_02
        |   |-- |-- |-- 0000
        |   |-- |-- |-- ...
        |-- |-- testing
        |-- label_02
        |   |-- 0000.txt
        |   |-- ...

  • Run python convert_kittitrack_to_coco.py in tools to convert the annotation into COCO format.

  • The resulting data structure should look like:

    ${RAM_ROOT}
|-- data
`-- |-- kitti_tracking
    `-- |-- data_tracking_image_2
        |   |-- training
        |   |   |-- image_02
        |   |   |   |-- 0000
        |   |   |   |-- ...
        |-- |-- testing
        |-- label_02
        |   |-- 0000.txt
        |   |-- ...
        |-- data_tracking_calib
        |-- label_02_val_half
        |   |-- 0000.txt
        |   |-- ...
        |-- label_02_train_half
        |   |-- 0000.txt
        |   |-- ...
        `-- annotations
            |-- tracking_train.json
            |-- tracking_test.json
            |-- tracking_train_half.json
            `-- tracking_val_half.json

To convert the annotation in a suitable format for evaluating track AP, run this command in tools: python convert_kitti_to_tao.py

LA-CATER

Is a toy synthetic obejct permanence benchmark. You can download the frames from the corresponding project web page. After downloading, copy the contents into $RAM_ROOT/data/la_cater. The annotations are avaiable under this link and should be palced under $RAM_ROOT/data/la_cater/annotations.

LA-CATER-Moving

Is our extrension of LA-CATER with a moving camera. You can download the frames and annotations under this link. After downloading, copy the contents into $RAM_ROOT/data/la_cater_moving.

References

Please cite the corresponding References if you use the datasets.

@inproceedings{tokmakov2021learning,
  title={Learning to Track with Object Permanence},
  author={Tokmakov, Pavel and Li, Jie and Burgard, Wolfram and Gaidon, Adrien},
  booktitle={ICCV},
  year={2021}
}

@INPROCEEDINGS{Geiger2012CVPR,
    author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
    title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
    booktitle = {CVPR},
    year = {2012}
}

@inproceedings{shamsian2020learning,
  title={Learning Object Permanence from Video},
  author={Shamsian, Aviv and Kleinfeld, Ofri and Globerson, Amir and Chechik, Gal},
  booktitle={ECCV},
  year={2020}
}

@inproceedings{girdhar2019cater,
  title={{CATER}: A diagnostic dataset for Compositional Actions and TEmporal Reasoning},
  author={Girdhar, Rohit and Ramanan, Deva},
  booktitle={ICLR},
  year={2020}
}