Tracking and Segmentation of Transparent Objects

Transparent object tracking is a challenging, recently introduced, problem. Existing methods predict target location as a bounding box, which is often only a poor approximation of actual location. Segmentation mask is a more accurate prediction, but benchmarks for evaluating tracking and segmentation performance of transparent objects does not exist. In this paper we address this drawback by introducing a new dataset for tracking and segmentation of transparent objects. In particular we sparsely re-annotate the existing bounding box TOTB dataset with ground-truth segmentation masks. A comprehensive analysis demonstrates that existing segmentation methods perform surprisingly well on this task indicating good design generalization and potential for transparent object tracking tasks. In addition, we show that existing bounding box trackers can be easily transformed into segmentation trackers using modern mask refinement methods.