참고 : The Basics of Video Object Segmentation

1. Dataset

It aims to recreate real-life video scenarios such as camera shake, background clutter, occlusions and other complexities.

about 150 short videos
The GyGO dataset specializes in smartphone captured videos and its frames are relatively sparse (the annotated video speed is ~5 fps).

There is a severe lack of data in the space of video object segmentation at the moment. With only hundreds of annotated videos, we believe every contribution has the potential to increase performance. In our analysis we have shown that a joint training on the GyGO and DAVIS datasets yields improved inference results.
To promote a more open, sharing culture and encourage other researchers to join our efforts :) The DAVIS dataset and the research ecosystem that grew it have been massively useful for us. We hope the community will find our datasets useful as well.

2. DAVIS-2017 challenge & Approaches

All of the leading works are based on either MaskTrack or OSVOS.
On the DAVIS-2017 challenge, MaskTrack has won.
Lucid Data Dreaming augmentations are becoming popular (read more below).
About half of the works have upgraded their base network to RESNET.
Almost everyone used some form of temporal component, leveraging the tendency of consecutive video frames to be similar.
About half of the works made use of a semantic component, employing semantic segmentation or detection (bounding box) networks in their solution.

Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, Computer Vision and Pattern Recognition (CVPR) 2016
The 2017 DAVIS Challenge on Video Object Segmentation J. Pont-Tuset, F. Perazzi, S. Caelles, P. Arbeláez, A. Sorkine-Hornung, and L. Van Gool, arXiv:1704.00675, 2017