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

Objects do not disappear: Video object detection by single-frame object location anticipation