  Robust object tracking requires knowledge and understanding of the object
being tracked: its appearance, its motion, and how it changes over time. A
tracker must be able to modify its underlying model and adapt to new
observations. We present Re3, a real-time deep object tracker capable of
incorporating temporal information into its model. Rather than focusing on a
limited set of objects or training a model at test-time to track a specific
instance, we pretrain our generic tracker on a large variety of objects and
efficiently update on the fly; Re3 simultaneously tracks and updates the
appearance model with a single forward pass. This lightweight model is capable
of tracking objects at 150 FPS, while attaining competitive results on
challenging benchmarks. We also show that our method handles temporary
occlusion better than other comparable trackers using experiments that directly
measure performance on sequences with occlusion.
