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Context-adaptive matching for optical flow

文献类型：期刊论文

作者Bao, Xiuguo3; Zu, Yueran4; Gao, Ke1; Wang, Yanyang2; Tang, Wenzhong4
刊名MULTIMEDIA TOOLS AND APPLICATIONS
出版日期2019
卷号78期号:1页码:641-659
关键词Optical flow PatchMatch Edge preserving Large displacement
ISSN号1380-7501
DOI10.1007/s11042-017-5386-2
英文摘要Modern sparse-to-dense optical flow estimation algorithms usually achieve state-of-art performance. Those algorithms need two steps: matching and interpolation. Matching is often unreliable for very large displacement optical flow due to illumination changes, deformations and occlusion etc. Moreover, conspicuous errors around motion discontinuities still keep serious as most methods consider edge only at interpolation step. The context-adaptive matching (CAM) is proposed for optical flow which is better at large displacement and edge preserving. The CAM is selective in feature extraction, adaptive in flow propagation and search radius adjusting. Selective features are proposed to consider edge preserving in matching step. Except for the usually used SIFT descriptor, the local directional pattern flow (LDPF) is introduced to keep more edge structure, and the oriented fast and rotated brief (ORB) is utilized to select out several most similar candidates. Unlike coarse-to-fine matching, which proposed a propagation step with only neighbors, we propose adaptive propagation to extend the matching candidates in order to improve the possibility of getting right correspondences. Furthermore, guided by prior knowledge and taking advantage of upper layers results, adaptive radius instead of constrained radius are proposed at finer layers. The CAM interpolated by EpicFlow is fast and robust for large displacements especially for fast moving objects and also preserves the edge structure well. Extensive experiments show that our algorithm is on par with the state-of-art optical flow methods on MPI-Sintel, KITTI and Middlebury.
资助项目Beijing Municipal Science and Technology Commission Project[Z171100000117010] ; National Key Research and Development Plan[2016YFB0801203] ; National Key Research and Development Plan[2016YFB0801200] ; National Nature Science Foundation of China[61271428]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000457317500036
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/3458]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Gao, Ke
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
2.Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China
3.Coordinat Ctr China CNCERT, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China
4.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Bao, Xiuguo,Zu, Yueran,Gao, Ke,et al. Context-adaptive matching for optical flow[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(1):641-659.
APA Bao, Xiuguo,Zu, Yueran,Gao, Ke,Wang, Yanyang,&Tang, Wenzhong.(2019).Context-adaptive matching for optical flow.MULTIMEDIA TOOLS AND APPLICATIONS,78(1),641-659.
MLA Bao, Xiuguo,et al."Context-adaptive matching for optical flow".MULTIMEDIA TOOLS AND APPLICATIONS 78.1(2019):641-659.

入库方式： OAI收割

来源：计算技术研究所

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