Datasets/ DAVIS: Densely Annotated VIdeo Segmentation

DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes. Each video is accompanied by densely annotated, pixel-accurate and per-frame ground truth segmentation.

Related publications:

  • J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbelaez, A. Sorkine-Hornung, and L. Van Gool, “The 2017 DAVIS Challenge on Video Object Segmentation”, 2017 PDF
  • F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation”, Computer Vision and Pattern Recognition (CVPR) 2016 PDF