  Current state-of-the-art trackers often fail due to distractorsand large
object appearance changes. In this work, we explore the use ofdense optical
flow to improve tracking robustness. Our main insight is that, because flow
estimation can also have errors, we need to incorporate an estimate of flow
uncertainty for robust tracking. We present a novel tracking framework which
combines appearance and flow uncertainty information to track objects in
challenging scenarios. We experimentally verify that our framework improves
tracking robustness, leading to new state-of-the-art results. Further, our
experimental ablations shows the importance of flow uncertainty for robust
tracking.
