  Objects in videos are typically characterized by continuous smooth motion. We
exploit continuous smooth motion in three ways. 1) Improved accuracy by using
object motion as an additional source of supervision, which we obtain by
anticipating object locations from a static keyframe. 2) Improved efficiency by
only doing the expensive feature computations on a small subset of all frames.
Because neighboring video frames are often redundant, we only compute features
for a single static keyframe and predict object locations in subsequent frames.
3) Reduced annotation cost, where we only annotate the keyframe and use smooth
pseudo-motion between keyframes. We demonstrate computational efficiency,
annotation efficiency, and improved mean average precision compared to the
state-of-the-art on four datasets: ImageNet VID, EPIC KITCHENS-55,
YouTube-BoundingBoxes, and Waymo Open dataset. Our source code is available at
https://github.com/L-KID/Videoobject-detection-by-location-anticipation.
