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Visual object tracking has focused predominantly on opaque objects, whiletransparent object tracking received very little attention. Motivated by theuniqueness of transparent objects in that their appearance is directly affectedby the background, the first dedicated evaluation dataset has emerged recently.We contribute to this effort by proposing the first transparent object trackingtraining dataset Trans2k that consists of over 2k sequences with 104,343 imagesoverall, annotated by bounding boxes and segmentation masks. Noting thattransparent objects can be realistically rendered by modern renderers, wequantify domain-specific attributes and render the dataset containing visualattributes and tracking situations not covered in the existing object trainingdatasets. We observe a consistent performance boost (up to 16%) across adiverse set of modern tracking architectures when trained using Trans2k, andshow insights not previously possible due to the lack of appropriate trainingsets. The dataset and the rendering engine will be publicly released to unlockthe power of modern learning-based trackers and foster new designs intransparent object tracking.

Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking