  Visual object tracking (VOT) is an essential component for many applications,
such as autonomous driving or assistive robotics. However, recent works tend to
develop accurate systems based on more computationally expensive feature
extractors for better instance matching. In contrast, this work addresses the
importance of motion prediction in VOT. We use an off-the-shelf object detector
to obtain instance bounding boxes. Then, a combination of camera motion
decouple and Kalman filter is used for state estimation. Although our baseline
system is a straightforward combination of standard methods, we obtain
state-of-the-art results. Our method establishes new state-of-the-art
performance on VOT (VOT-2016 and VOT-2018). Our proposed method improves the
EAO on VOT-2016 from 0.472 of prior art to 0.505, from 0.410 to 0.431 on
VOT-2018. To show the generalizability, we also test our method on video object
segmentation (VOS: DAVIS-2016 and DAVIS-2017) and observe consistent
improvement.
