Re$^3$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

@article{Gordon2018Re3RA,
  title={Re\$^3\$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects},
  author={Daniel Gordon and Ali Farhadi and Dieter Fox},
  journal={IEEE Robotics and Automation Letters},
  year={2018},
  volume={3},
  pages={788-795}
}
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 Re<inline-formula><tex-math notation="LaTeX">$^3,$</tex-math></inline-formula> 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… CONTINUE READING
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