Key Instance Selection for Unsupervised Video Object Segmentation

This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their simila...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Cho, Donghyeon, Hong, Sungeun, Kang, Sungil, Kim, Jiwon
Format: Article
Language:English
Subjects:
Masks
Salience
Segmentation
Similarity
Online Access:Get full text
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Summary:This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their similarity until the M-th frame and then assign them unique IDs (i.e., instances). Similarity measure takes into account multiple properties such as ReID descriptor, expected trajectory, and semantic co-segmentation result. After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames. Thanks to these technical contributions, our results are ranked third on the leaderboard of UVOS DAVIS challenge.
ISSN:2331-8422