Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks
Abstract
In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets.
Cite
Text
Li et al. "Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01219-9_13Markdown
[Li et al. "Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/li2018eccv-unsupervised/) doi:10.1007/978-3-030-01219-9_13BibTeX
@inproceedings{li2018eccv-unsupervised,
title = {{Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks}},
author = {Li, Siyang and Seybold, Bryan and Vorobyov, Alexey and Lei, Xuejing and Jay Kuo, C.-C.},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01219-9_13},
url = {https://mlanthology.org/eccv/2018/li2018eccv-unsupervised/}
}