  The best RGBD trackers provide high accuracy but are slow to run. On the
other hand, the best RGB trackers are fast but clearly inferior on the RGBD
datasets. In this work, we propose a deep depth-aware long-term tracker that
achieves state-of-the-art RGBD tracking performance and is fast to run. We
reformulate deep discriminative correlation filter (DCF) to embed the depth
information into deep features. Moreover, the same depth-aware correlation
filter is used for target re-detection. Comprehensive evaluations show that the
proposed tracker achieves state-of-the-art performance on the Princeton RGBD,
STC, and the newly-released CDTB benchmarks and runs 20 fps.
