Abstract
Square fiducial markers are a popular tool for camera pose estimation because of their high robustness and performance. However, state-of-the-art methods perform poorly under difficult image conditions, such as camera defocus, motion blur, small scale or non-uniform lighting. This paper tackles the marker identification problem as a classification one, proposing a methodology to train such classifiers by creating a synthetic dataset of markers affected by several transformations. Our approach employes a SVM for marker identification. Statistical analyses have been performed in order to determine the best SVM configuration for our problem, and the best one is compared to the ArUco fiducial marker systems in challenging video sequences. The results obtained show that the proposed method performs significantly better.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, P., Peng, Z., Li, D., Yang, L.: An improved augmented reality system based on AndAR. J. Vis. Commun. Image Represent. 37, 63–69 (2016)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Fiala, M.: Designing highly reliable fiducial markers. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1317–1324 (2010)
Finner, H.: On a monotonicity problem in step-down multiple test procedures. J. Am. Stat. Assoc. 88(423), 920–923 (1993)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)
Garrido-Jurado, S., Muñoz Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014)
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Medina-Carnicer, R.: Generation of fiducial marker dictionaries using mixed integer linear programming. Pattern Recogn. 51, 481–491 (2016)
Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, MIR 2008, pp. 39–43. ACM, New York (2008)
Khattak, S., Cowan, B., Chepurna, I., Hogue, A.: A real-time reconstructed 3D environment augmented with virtual objects rendered with correct occlusion. In: IEEE Games Media Entertainment (GEM), pp. 1–8. IEEE (2014)
Olivares-Mendez, M.A., Kannan, S., Voos, H.: Vision based fuzzy control autonomous landing with uavs: from V-rep to real experiments. In: 23th Mediterranean Conference on Control and Automation (MED), pp. 14–21, June 2015
Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3400–3407 (2011)
Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(6), 559–572 (1901)
Rumpler, M., Daftry, S., Tscharf, A., Prettenthaler, R., Hoppe, C., Mayer, G., Bischof, H.: Automated end-to-end workflow for precise and geo-accurate reconstructions using fiducial markers. ISPRS Ann. Photogrammetry Remote Sens. Spat. Inf. Sci. 3, 135–142 (2014)
Wagner, D., Schmalstieg, D.: ARToolKitPlus for pose tracking on mobile devices. In: Computer Vision Winter, Workshop, pp. 139–146 (2007)
Acknowledgments
This project has been funded under projects TIN2016-75279-P, RTC-2016-5661-1 and by the “Programa propio XXI de Investigacion” of the University of Cordoba.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mondéjar-Guerra, V.M., Garrido-Jurado, S., Muñoz-Salinas, R., Marín-Jiménez, M.J., Medina-Carnicer, R. (2017). Classification of Fiducial Markers in Challenging Conditions with SVM. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-58838-4_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58837-7
Online ISBN: 978-3-319-58838-4
eBook Packages: Computer ScienceComputer Science (R0)