中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 视频内容安全团队
Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection

文献类型：期刊论文

作者Zhang, Congxuan1,2; Feng, Cheng2; Chen, Zhen2; Hu, Weiming1; Li, Ming2
刊名SIGNAL PROCESSING-IMAGE COMMUNICATION
出版日期2022-02-01
卷号101页码:14
关键词Optical flow Occlusion detection Edge-preserving Parallel multiscale context Convolutional neural network
ISSN号0923-5965
DOI10.1016/j.image.2021.116560
通讯作者Chen, Zhen(dr_chenzhen@163.com)
英文摘要Although convolutional neural network (CNN)-based optical flow approaches have exhibited good performance in terms of computational accuracy and efficiency in recent years, the issue of edge-blurring caused by motion occlusions remains. In this paper, we propose a parallel multiscale context-based edge-preserving optical flow estimation method with occlusion detection, named PMC-PWC. First, we exploit a parallel multiscale context (PMC) network for occlusion detection, in which the proposed PMC model is able to aggregate the multiscale context information to develop the performance of occlusion detection near motion boundaries. Second, we combine the PMC model with a context network to plan an occlusion estimation module and incorporate it into a pyramid, warping, and cost volume model to construct an edge-preserving optical flow computation network. Third, we design a novel loss function including an endpoint error (EPE)-based loss, a binary cross-entropy loss and an edge loss to supervise the proposed PMC-PWC network to produce optical flow and occlusion simultaneously. Finally, we run the proposed PMC-PWC method on the MPI-Sintel and KITTI datasets to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results indicate that the proposed PMC-PWC method performed well in terms of both accuracy and robustness, especially due to the significant benefits of edge preservation and occlusion handling.
资助项目National Key Research and Development Program of China[2020YFC2003800] ; National Natural Science Foundation of China[61772255] ; National Natural Science Foundation of China[61866026] ; National Natural Science Foundation of China[61866025] ; Advantage Subject Team Project of Jiangxi Province, China[20165BCB19007] ; Outstanding Young Talents Program of Jiangxi Province, China[20192BCB23011] ; National Natural Science Foundation of Jiangxi Province, China[20202ACB214007] ; Aeronautical Science Foundation of China[2018ZC56008] ; China Postdoctoral Science Foundation[2019M650894]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000724345500006
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Advantage Subject Team Project of Jiangxi Province, China ; Outstanding Young Talents Program of Jiangxi Province, China ; National Natural Science Foundation of Jiangxi Province, China ; Aeronautical Science Foundation of China ; China Postdoctoral Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/46575]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Chen, Zhen
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Nanchang Hangkong Univ, Key Lab Nondestruct Testing, Minist Educ, Nanchang 330063, Jiangxi, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Congxuan,Feng, Cheng,Chen, Zhen,et al. Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection[J]. SIGNAL PROCESSING-IMAGE COMMUNICATION,2022,101:14.
APA Zhang, Congxuan,Feng, Cheng,Chen, Zhen,Hu, Weiming,&Li, Ming.(2022).Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection.SIGNAL PROCESSING-IMAGE COMMUNICATION,101,14.
MLA Zhang, Congxuan,et al."Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection".SIGNAL PROCESSING-IMAGE COMMUNICATION 101(2022):14.

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。