Deep learning for video object segmentation: a review

Gao，Mingqi1,2; Zheng，Feng2; Yu，James J.Q.2; Shan，Caifeng3; Ding，Guiguang4; Han，Jungong1,5

2022

10.1007/s10462-022-10176-7

ARTIFICIAL INTELLIGENCE REVIEW   影响因子和分区

0269-2821

1573-7462

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.

Convolutional neural network Deep learning Video object segmentation

SCI ; EI

英语

其他

Computer Science

Computer Science, Artificial Intelligence

WOS:000781305100001

SPRINGER

20221511947307

Convolutional neural networks ; Deep neural networks ; Motion compensation ; Well testing

Ergonomics and Human Factors Engineering:461.4

COMPUTER SCIENCE

2-s2.0-85127648745

Scopus

1.WMG Data Science,University of Warwick,Coventry,CV4 7AL,United Kingdom
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao,266590,China
4.School of Software,Tsinghua University,Beijing,100084,China
5.Department of Computer Science,Aberystwyth University,Aberystwyth,SY23 3DB,United Kingdom

Gao，Mingqi,Zheng，Feng,Yu，James J.Q.,et al. Deep learning for video object segmentation: a review[J]. ARTIFICIAL INTELLIGENCE REVIEW,2022,56(1):457-531.

Gao，Mingqi,Zheng，Feng,Yu，James J.Q.,Shan，Caifeng,Ding，Guiguang,&Han，Jungong.(2022).Deep learning for video object segmentation: a review.ARTIFICIAL INTELLIGENCE REVIEW,56(1),457-531.

Gao，Mingqi,et al."Deep learning for video object segmentation: a review".ARTIFICIAL INTELLIGENCE REVIEW 56.1(2022):457-531.