Multi-Object Tracking with Hallucinated and Unlabeled Videos

Understand

In this paper, we explore learning end-to-end deep neural trackers without tracking annotations.

• This is important as large-scale training data is essential for training deep neural trackers while tracking annotations are expensive to acquire.
• In place of tracking annotations, we first hallucinate videos from images with bounding box annotations using zoom-in/out motion transformations to obtain free tracking labels.
• We add video simulation augmentations to create a diverse tracking dataset, albeit with simple motion.