Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects
文献类型：期刊论文
| 作者 | Rui Jiang3,4; Ruixiang Zhu3; Hu Su2 ; Yinlin Li1; Yuan Xie4; Wei Zou2
|
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2023 |
| 卷号 | 20期号:3页码:335-369 |
| 关键词 | Moving object segmentation (MOS), change detection, background subtraction, deep learning (DL), video understanding |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-022-1378-4 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55984]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 3.College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China 4.School of Computer Science and Technology, East China Normal University, Shanghai 200062, 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。