Objects do not disappear
Video object detection by single-frame object location anticipation
X. Liu (TU Delft - Pattern Recognition and Bioinformatics)
Jan Van Gemert (TU Delft - Pattern Recognition and Bioinformatics)
Fatemeh Karimi Nejadasl (Universiteit van Amsterdam)
O. Booij (TU Delft - Pattern Recognition and Bioinformatics)
Silvia L. Pintea (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
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/Video-object-detection-by-location-anticipation.