CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit
Khan, Dawar1,2,3; Ullah, Sehat3; Yan, Dong-Ming1,2; Rabbi, Ihsan3; Richard, Paul4; Hoang, Thuong4; Billinghurst, Mark5; Zhang, Xiaopeng1,2; Sehat Ullah and XIAOPENG ZHANG
Source PublicationIEEE ACCESS
2018
Volume6Issue:1Pages:22421-22433
SubtypeArticle
AbstractFiducial markers are images or landmarks placed in real environment, typically used for pose estimation and camera tracking. Reliable fiducials are strongly desired for many augmented reality (AR) applications, but currently there is no systematic method to design highly reliable fiducials. In this paper, we present fiducial marker optimizer (FMO), a tool to optimize the design attributes of ARToolKit markers, including black to white (B:W) ratio, edge sharpness, and information complexity, and to reduce inter-marker confusion. For these operations, the FMO provides a user friendly interface at the front-end and specialized image processing algorithms at the back-end. We tested manually designed markers and FMO optimized markers in ARToolKit and found that the latter were more robust. The FMO will be used for designing highly reliable fiducials in easy to use fashion. It will improve the application's performance, where it is used.
KeywordFiducial Markers Artoolkit Augmented Reality Marker Tracking Robust Recognition
WOS HeadingsScience & Technology ; Technology
DOI10.1109/ACCESS.2018.2801028
WOS KeywordAUTOMATIC-GENERATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61772523 ; Chinese Government Scholarship ; 61620106003 ; 61331018)
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000432577200001
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21477
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorSehat Ullah and XIAOPENG ZHANG
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Malakand, Dept Comp Sci & IT, Chakdara 18800, Pakistan
4.Univ Angers, Lab Angevin Rech Ingn Syst, F-49035 Angers, France
5.Univ South Australia, Sch ITMS, Adelaide, SA 5095, Australia
Recommended Citation
GB/T 7714
Khan, Dawar,Ullah, Sehat,Yan, Dong-Ming,et al. Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit[J]. IEEE ACCESS,2018,6(1):22421-22433.
APA Khan, Dawar.,Ullah, Sehat.,Yan, Dong-Ming.,Rabbi, Ihsan.,Richard, Paul.,...&Sehat Ullah and XIAOPENG ZHANG.(2018).Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit.IEEE ACCESS,6(1),22421-22433.
MLA Khan, Dawar,et al."Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit".IEEE ACCESS 6.1(2018):22421-22433.
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