Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects

doi:10.1007/s11633-022-1378-4

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects
	Rui Jiang 1,2; Ruixiang Zhu 1; Hu Su3 ; Yinlin Li4 ; Yuan Xie2 ; Wei Zou3
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2023
卷号	20 期号:3 页码:335-369
摘要	Moving object segmentation (MOS), aiming at segmenting moving objects from video frames, is an important and challenging task in computer vision and with various applications. With the development of deep learning (DL), MOS has also entered the era of deep models toward spatiotemporal feature learning. This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years. Specifically, we present a more up-to-date categorization based on model characteristics, then compare and discuss each category from feature learning (FL), and model training and evaluation perspectives. For FL, the methods reviewed are divided into three types: spatial FL, temporal FL, and spatiotemporal FL, then analyzed from input and model architectures aspects, three input types, and four typical preprocessing subnetworks are summarized. In terms of training, we discuss ideas for enhancing model transferability. In terms of evaluation, based on a previous categorization of scene dependent evaluation and scene independent evaluation, and combined with whether used videos are recorded with static or moving cameras, we further provide four subdivided evaluation setups and analyze that of reviewed methods. We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology. Finally, based on the above comparisons and discussions, we present research prospects and future directions.
关键词	Moving object segmentation (MOS), change detection, background subtraction, deep learning (DL), video understanding
DOI	10.1007/s11633-022-1378-4
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55984
专题	学术期刊_Machine Intelligence Research
作者单位	1.College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China 2.School of Computer Science and Technology, East China Normal University, Shanghai 200062, China 3.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 4.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Rui Jiang,Ruixiang Zhu,Hu Su,et al. Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects[J]. Machine Intelligence Research,2023,20(3):335-369.
APA	Rui Jiang,Ruixiang Zhu,Hu Su,Yinlin Li,Yuan Xie,&Wei Zou.(2023).Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects.Machine Intelligence Research,20(3),335-369.
MLA	Rui Jiang,et al."Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects".Machine Intelligence Research 20.3(2023):335-369.

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MIR-2022-06-191.pdf（9061KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览