  Visual object tracking has focused predominantly on opaque objects, while
transparent object tracking received very little attention. Motivated by the
uniqueness of transparent objects in that their appearance is directly affected
by the background, the first dedicated evaluation dataset has emerged recently.
We contribute to this effort by proposing the first transparent object tracking
training dataset Trans2k that consists of over 2k sequences with 104,343 images
overall, annotated by bounding boxes and segmentation masks. Noting that
transparent objects can be realistically rendered by modern renderers, we
quantify domain-specific attributes and render the dataset containing visual
attributes and tracking situations not covered in the existing object training
datasets. We observe a consistent performance boost (up to 16%) across a
diverse set of modern tracking architectures when trained using Trans2k, and
show insights not previously possible due to the lack of appropriate training
sets. The dataset and the rendering engine will be publicly released to unlock
the power of modern learning-based trackers and foster new designs in
transparent object tracking.
