A New Dataset and a Distractor-Aware Architecture for Transparent Object
  Tracking
Alan Lukezic Ziga Trojer Jiri Matas Matej Kristan
Performance of modern trackers degrades substantially on transparent objects
compared to opaque objects. This is largely due to two distinct reasons.
Transparent objects are unique in that their appearance is directly affected by
the background. Furthermore, transparent object scenes often contain many
visually similar objects (distractors), which often lead to tracking failure.
However, development of modern tracking architectures requires large training
sets, which do not exist in transparent object tracking. We present two
contributions addressing the aforementioned issues. We propose 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. Standard trackers trained on this dataset consistently
improve by up to 16%. Our second contribution is a new distractor-aware
transparent object tracker (DiTra) that treats localization accuracy and target
identification as separate tasks and implements them by a novel architecture.
DiTra sets a new state-of-the-art in transparent object tracking and
generalizes well to opaque objects.