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Robust object tracking requires knowledge and understanding of the objectbeing tracked: its appearance, its motion, and how it changes over time. Atracker must be able to modify its underlying model and adapt to newobservations. We present Re3, a real-time deep object tracker capable ofincorporating temporal information into its model. Rather than focusing on alimited set of objects or training a model at test-time to track a specificinstance, we pretrain our generic tracker on a large variety of objects andefficiently update on the fly; Re3 simultaneously tracks and updates theappearance model with a single forward pass. This lightweight model is capableof tracking objects at 150 FPS, while attaining competitive results onchallenging benchmarks. We also show that our method handles temporaryocclusion better than other comparable trackers using experiments that directlymeasure performance on sequences with occlusion.

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects