A deep-learning-based transparent object trackerTrojer, Žiga (Avtor) Kristan, Matej (Mentor) Lukežič, Alan (Komentor) computer visiontrackingtransparent objectdatasetVisual 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 datasets Trans2k and Trans1k, which include 156,143 total images across over 3k sequences and are annotated with segmentation masks and bounding boxes. 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. In addition, we propose a new transparent object tracker DiETTO, which sets a new state-of-the-art on the recent transparent transparent object tracking benchmark. The datasets, rendering engine and DiETTO will be publicly released, contributing to the efforts toward unlocking the power of modern learning-based trackers and foster new designs in transparent object tracking.20222022-09-05 11:55:00Magistrsko delo/naloga139585sl