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Template-based discriminative trackers are currently the dominant trackingparadigm due to their robustness, but are restricted to bounding box trackingand a limited range of transformation models, which reduces their localizationaccuracy. We propose a discriminative single-shot segmentation tracker - D3S,which narrows the gap between visual object tracking and video objectsegmentation. A single-shot network applies two target models withcomplementary geometric properties, one invariant to a broad range oftransformations, including non-rigid deformations, the other assuming a rigidobject to simultaneously achieve high robustness and online targetsegmentation. Without per-dataset finetuning and trained only for segmentationas the primary output, D3S outperforms all trackers on VOT2016, VOT2018 andGOT-10k benchmarks and performs close to the state-of-the-art trackers on theTrackingNet. D3S outperforms the leading segmentation tracker SiamMask on videoobject segmentation benchmark and performs on par with top video objectsegmentation algorithms, while running an order of magnitude faster, close toreal-time.

D3S -- A Discriminative Single Shot Segmentation Tracker