Comparison on video object segmentation: methods and results

Yifei Liu; Yuzhe Wang; Wenhui Wang; Minda Zhang

doi:10.1117/12.2631435

18 March 2022 Comparison on video object segmentation: methods and results

Yifei Liu, Yuzhe Wang, Wenhui Wang, Minda Zhang

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121681R (2022) https://doi.org/10.1117/12.2631435
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

Video object segmentation (VOS) is a fundamental research area in the computer vision field in which the goal is to separate the target object(s) from the background in continuous frames of a video. It has huge application value and demand in human-computer interaction, video analysis, compression, re-creation, and numerous fields. Current VOS research in CV academia is mainly classified into four main categories: semi-supervised VOS, unsupervised VOS, weakly supervised VOS, and interactive VOS. In this paper, we give an overview of the latest methods in the first three categories. We summarized problems in each area and features of different methods aiming to solve them. Then we compare these methods to find out the performance in different test environments.

Citation Download Citation

Yifei Liu, Yuzhe Wang, Wenhui Wang, and Minda Zhang "Comparison on video object segmentation: methods and results", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121681R (18 March 2022); https://doi.org/10.1117/12.2631435

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