Abstract
This paper presents a method for real-time single-color marker detection. The algorithm is based on our previous work, and the goal of this paper is to investigate and test possible enhancements that can be done, namely color weighting, hue calculation, and modified versions of dilatation and erosion operations. The paper also explains a solution for dynamic color selection, which makes the system more robust to varying lighting conditions by detecting the color hue, saturation, and value under the current conditions. The designed methods are implemented in WebGL, which allows running the developed application on any platform with any operating system, given WebGL is supported by the web browser. Testing of all described and implemented improvements was conducted, and it revealed that using hue weighting has a good effect on the resulting detection. On the other hand, dynamic thresholding of saturation and value components of the HSV color model does not give good results. Therefore, testing for finding the right threshold for these components had to be done. The erosion operation improves the detection significantly while dilatation does not have almost any impact on the result.
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References
Fiala, M.: ARTag, a fiducial marker system using digital techniques. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005). pp. 590–596. IEEE, San Diego (2005). https://doi.org/10.1109/CVPR.2005.74
KATO, H.: ARToolKit : Library for vision-based augmented reality. Tech. Rep. IEICE PRMU. 101, pp. 79–86 (2002)
Fiala, M.: Comparing ARTag and ARToolkit Plus fiducial marker systems. In: IEEE International Workshop on Haptic Audio Visual Environments and their Applications. p. 6 (2005). https://doi.org/10.1109/HAVE.2005.1545669
Shabalina, K., Sagitov, A., Sabirova, L., Li, H., Magid, E.: ARTag, AprilTag and CALTag fiducial systems comparison in a presence of partial rotation: manual and automated approaches. In: Gusikhin, O., Madani, K. (eds.) ICINCO 2017. LNEE, vol. 495, pp. 536–558. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-11292-9_27
Hirzer, M.: Marker detection for augmented reality applications. (2008)
Xiang, Z., Fronz, S., Navab, N.: Visual marker detection and decoding in AR systems: a comparative study. In: Proceedings International Symposium on Mixed and Augmented Reality. pp. 97–106. IEEE Computer Social Darmstadt, Germany (2002). https://doi.org/10.1109/ISMAR.2002.1115078
Susan, S., Tandon, S., Seth, S., Mudassir, Chaudhary, R., Baisoya, N.: Kullback-Leibler divergence based marker detection in augmented reality. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA). pp. 1–5 (2018). https://doi.org/10.1109/CCAA.2018.8777570
Gao, Q.H., Wan, T.R., Tang, W., Chen, L.: A stable and accurate marker-less augmented reality registration method. In: 2017 International Conference on Cyberworlds (CW). pp. 41–47 (2017). https://doi.org/10.1109/CW.2017.44
Chen, C.-W., Chen, W.-Z., Peng, J.-W., Cheng, B.-X., Pan, T.-Y., Kuo, H.-C.: A real-time markerless augmented reality framework based on slam technique. In: 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks 2017 11th International Conference on Frontier of Computer Science and Technology 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC). pp. 127–132 (2017). https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.87
Gupta, A., Bhatia, K., Gupta, K., Vardhan, M.: A comparative study of marker-based and marker-less indoor navigation in augmented reality. Int. Res. J. Eng. Technol. (IRJET) 5, 1–4 (2018)
Cheng, J.C.P., Chen, K., Chen, W.: Comparison of marker-based and markerless Ar: a case study of an indoor decoration system. In: Lean and Computing in Construction Congress - Volume 1: Proceedings of the Joint Conference on Computing in Construction. pp. 483–490, Greece (2017). https://doi.org/10.24928/JC3-2017/0231
Stridbar, L., Henriksson, E.: Subjective evaluation of marker-based and marker-less ar for an exhibition of a digitally recreated swedish warship. (2019)
Košt’ák, M., Ježek, B.: Mobile phone as an interactive device in augmented reality system. In: DIVAI 2018 (2018)
Košťák, M., Ježek, B., Slabý, A.: Color marker detection with webgl for mobile augmented reality systems. In: Awan, I., Younas, M., Ünal, P., Aleksy, M. (eds.) MobiWIS 2019. LNCS, vol. 11673, pp. 71–84. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27192-3_6
Lai, C.-L., Wang, C.-L.: Mobile edutainment with interactive augmented reality using adaptive marker tracking. In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems. pp. 124–131. IEEE, Singapore (2012). https://doi.org/10.1109/ICPADS.2012.27
Smith, R.T., Marner, M.R., Thomas, B.H.: An adaptive color marker for Spatial Augmented Reality environments and visual feedback. In: 2011 IEEE Virtual Reality Conference. pp. 269–270. IEEE, Singapore (2011). https://doi.org/10.1109/VR.2011.5759502
Smith, R.T., Marner, M.R., Thomas, B.H.: Adaptive color marker for SAR environments. In: 2011 IEEE Symposium on 3D User Interfaces (3DUI). pp. 119–120 (2011). https://doi.org/10.1109/3DUI.2011.5759234
Rasmussen, C., Toyama, K., Hager, G.D.: Tracking Objects By Color Alone. (1996)
Romero-Ramirez, F.J., Muñoz-Salinas, R., Medina-Carnicer, R.: Speeded up detection of squared fiducial markers. Image Vis. Comput. 76, 38–47 (2018). https://doi.org/10.1016/j.imavis.2018.05.004
Saaidon, N., Sediono, W., Sophian, A.: Altitude Tracking Using Colour Marker Based Navigation System for Image Guided Surgery. In: 2016 International Conference on Computer and Communication Engineering (ICCCE). pp. 465–469. IEEE, Kuala Lumpur, Malaysia (2016). https://doi.org/10.1109/ICCCE.2016.103
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE T. Syst. Man. Cy. B. 9(1), 62–66 (1979)
Liao, P.-S., Chen, T.-S., Chung, P.-C.: A Fast Algorithm for Multilevel Thresholding. (2001)
Huang, D.-Y., Wang, C.-H.: Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recognit. Lett. 30, 275–284 (2009). https://doi.org/10.1016/j.patrec.2008.10.003
Kim, G., Petriu, E.M.: Fiducial marker indoor localization with artificial neural network. In: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. pp. 961–966. IEEE, Montreal, QC, Canada (2010). https://doi.org/10.1109/AIM.2010.5695801
Dash, A.K., Behera, S.K., Dogra, D.P., Roy, P.P.: Designing of marker-based augmented reality learning environment for kids using convolutional neural network architecture. Displays 55, 46–54 (2018). https://doi.org/10.1016/j.displa.2018.10.003
Acknowledgement
This work and the contribution were supported by a project of Students Grant Agency (SPEV) - FIM, University of Hradec Kralove, Czech Republic.
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Košťák, M., Ježek, B., Slabý, A. (2020). Adaptive Detection of Single-Color Marker with WebGL. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2020. Lecture Notes in Computer Science(), vol 12242. Springer, Cham. https://doi.org/10.1007/978-3-030-58465-8_29
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