  Template-based discriminative trackers are currently the dominant tracking
paradigm due to their robustness, but are restricted to bounding box tracking
and a limited range of transformation models, which reduces their localization
accuracy. We propose a discriminative single-shot segmentation tracker -- D3S2,
which narrows the gap between visual object tracking and video object
segmentation. A single-shot network applies two target models with
complementary geometric properties, one invariant to a broad range of
transformations, including non-rigid deformations, the other assuming a rigid
object to simultaneously achieve robust online target segmentation. The overall
tracking reliability is further increased by decoupling the object and feature
scale estimation. Without per-dataset finetuning, and trained only for
segmentation as the primary output, D3S2 outperforms all published trackers on
the recent short-term tracking benchmark VOT2020 and performs very close to the
state-of-the-art trackers on the GOT-10k, TrackingNet, OTB100 and LaSoT. D3S2
outperforms the leading segmentation tracker SiamMask on video object
segmentation benchmarks and performs on par with top video object segmentation
algorithms.
