Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking
This addresses the problem of tracking transparent objects for computer vision researchers, providing a new dataset to enable better model training and design, though it is incremental as it builds on existing tracking methods.
The authors tackled the lack of training data for transparent object tracking by creating Trans2k, a dataset with over 2k sequences and 104,343 images, which boosted performance by up to 16% across modern tracking architectures.
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.