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Deep learning for video object segmentation : a review
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Gao, Mingqi, Zheng, Feng, Yu, James J. Q., Shan, Caifeng, Ding, Guiguang and Han, Jungong (2023) Deep learning for video object segmentation : a review. Artificial Intelligence Review, 56 . pp. 457-531. doi:10.1007/s10462-022-10176-7 ISSN 0269-2821.
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Official URL: http://dx.doi.org/10.1007/s10462-022-10176-7
Abstract
As one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural networks have shown outstanding performance improvements in many computer vision applications, with video object segmentation being one of the most advocated and intensively investigated. In this paper, we present a systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches. Concretely, we start by introducing the definition, background concepts and basic ideas of algorithms in this field. Subsequently, we summarise the datasets for training and testing a video object segmentation algorithm, as well as common challenges and evaluation metrics. Next, previous works are grouped and reviewed based on how they extract and use spatial and temporal features, where their architectures, contributions and the differences among each other are elaborated. At last, the quantitative and qualitative results of several representative methods on a dataset with many remaining challenges are provided and analysed, followed by further discussions on future research directions. This article is expected to serve as a tutorial and source of reference for learners intended to quickly grasp the current progress in this research area and practitioners interested in applying the video object segmentation methods to their problems. A public website is built to collect and track the related works in this field: https://github.com/gaomingqi/VOS-Review.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Library of Congress Subject Headings (LCSH): | Digital video, Artificial intelligence, MPEG (Video coding standard), Image processing -- Digital techniques, Deep learning (Machine learning), Neural networks (Computer science) | ||||||
Journal or Publication Title: | Artificial Intelligence Review | ||||||
Publisher: | Springer | ||||||
ISSN: | 0269-2821 | ||||||
Official Date: | January 2023 | ||||||
Dates: |
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Volume: | 56 | ||||||
Page Range: | pp. 457-531 | ||||||
DOI: | 10.1007/s10462-022-10176-7 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 21 April 2022 | ||||||
Date of first compliant Open Access: | 21 April 2022 |
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