Efficient Line-Based Visual Marker System Design with Occlusion Resilience

Abdallah Bengueddoudj, Foudil Belhadj, Yongtao Hu, Brahim Zitouni, Yacine Idir, Ibtissem Adoui, Messaoud Mostefai

Abstract


Today, the most widely used visual markers, such as ArUco and AprilTag, rely on square pixel arrays. While these markers can deliver satisfactory detection and identification outcomes, they remain vulnerable to corner occlusion despite incorporating corrective codes. Conversely, line-based markers offer increased resilience against occlusions but are typically constrained in terms of codification capacities. The markers developed in this research leverage linear information to propose a pyramidal line-based structure that exhibits robustness to corner occlusion while providing enhanced coding capacities. Moreover, the projective invariance of the constituent lines enables the validation of a homography-less identification method that considerably reduces computation resources and processing time. We assembled an extensive test dataset of 169,713 images for evaluation, including rotation, distances, and different levels of occlusion. Experiments on this dataset show that the proposed marker significantly outperforms previous fiducial marker systems across multiple metrics, including execution time and detection performance under occlusion. It effectively identifies markers with up to 50% occlusion and achieves identification at a resolution of 1920×1080 in 17.20 ms. The developed marker generation and identification, as well as an extensive marker Database, are publicly available for tests at https://github.com/OILUproject/OILUtag.

Full Text:

PDF

References


References

. Jayatilleke, L., Zhang, N. Landmark-based localization for unmanned aerial vehicles. IEEE International Systems Conference (SysCon), 2013, pp. 448–451. http://dx.doi.org/10.1109/SysCon.2013.6549921

. Romero-Ramire, F. J., Munoz-Salinas, R., Medina-Carnicer, R. Fractal Markers: a new approach for long-range marker pose estimation under occlusion. IEEE Access, (2019), 7, pp. 169908–169919. http://dx.doi.org/10.1109/ACCESS.2019.2951204

. Zhenglong, G., Qiang, F., & Quan, Q. Pose estimation for multicopters based on monocular vision and AprilTag, 37th Chinese Control Conference (CCC), 2018, pp. 4717–4722. http://dx.doi.org/10.23919/ChiCC.2018.8483685

. Hagbi, N., Bergig, O., El-Sana, J., & Billinghurst, M. Shape recognition and pose estimation for mobile augmented reality. IEEE Transactions on Visualization and Computer Graphics, 17(10), 2010, pp. 1369–1379. http://dx.doi.org/10.1109/TVCG.2010.241

. Sani, M. F., & Karimian, G. Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors. International Conference on Computer and Drone Applications (IConDA), 2017, 102–107. http://dx.doi.org/10.1109/ICONDA.2017.8270408

. Sarmadi, H., Muñoz-Salinas, R., M. Álvaro, B., Luna, A., Medina-Carnicer, R. 3D Reconstruction and alignment by consumer RGB-D sensors and fiducial planar markers for patient positioning in radiation therapy, Computer Methods and Programs in Biomedicine,Volume 180,2019,105004, https://doi.org/10.1016/j.cmpb.2019.105004

. Olson, E. AprilTag: A robust and flexible visual fiducial system. IEEE International Conference on Robotics and Automation, (2011), pp. 3400–3407. (2011) http://dx.doi.org/10.1109/ICRA.2011.5979561

. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., & Marín-Jiménez, M. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47, 2280–2292. (2014). http://dx.doi.org/10.1016/j.patcog.2014.01.005

. Rhijn, A., Jurriaan, M. Optical Tracking using Line Pencil Fiducials. (2004), 10.2312/EGVE/EGVE04/035-044.

. Chahir, Y., Mostefai, M., & Saidat, H. New Efficient Visual OILU Marker, The 25th International Conference on Image Processing Computer Vision, & Pattern Recognition (IPCV 2021), Book of Abstracts, 138, ISBN # 1-60132-514-2, (2021).

. Adalsteinsson, D., Sethian, J. A Fast Level Set Method For Propagating Interfaces, Comp Phys., (1995), Vol. 118, pp. 269-277. doi:10.1006/jcph.1995.1098

. Kato, H., & Billinghurst, M. Marker tracking and hmd calibration for a video-based augmented reality conferencing system. Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR’99),(1999), pp. 85–94. http://dx.doi.org/10.1109/IWAR.1999.803809

. Fiala, M. ARTag, a fiducial marker system using digital techniques. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), (2005), pp. 590–596. http://dx.doi.org/10.1109/CVPR.2005.74

. Fiala, M. Comparing ARTag and ARToolkit Plus fiducial marker systems. IEEE International Workshop on Haptic Audio Visual Environments and Their Applications, (2005). 6--pp. http://dx.doi.org/10.1109/HAVE.2005.1545669

. Yu, G., & Hu, Y., & Dai, J. TopoTag: A Robust and Scalable Topological Fiducial Marker System. IEEE Transactions on Visualization and Computer Graphics. (2020). http://dx.doi.org/10.48550/arXiv.1908.01450

. DeGol, J., Bretl, T., & Hoiem, D. Chromatag: A colored marker and fast detection algorithm. Proceedings of the IEEE International Conference on Computer Vision, (2017), pp. 1472–1481. https://doi.org/10.1109/ICCV.2017.16

. Calvet, L., Gurdjos, P., Griwodz, C., Gasparini, S. Detection and accurate localization of circular fiducials under highly challenging conditions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 562–570. http://dx.doi.org/10.1109/CVPR.2016.67

. Bergamasco, F., Albarelli, A., Torsello, A. Pi-tag: a fast image-space marker design based on projective invariants. Machine vision and applications, (2013), 24(6):1295–1310. http://dx.doi.org/10.1007/s00138-012-0469-6

. Bergamasco, F., Albarelli, A., Rodolà, E., Torsello, A. RUNE-Tag: A high accuracy fiducial marker with strong occlusion resilience. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (2011), pp.113 - 120. http://dx.doi.org/10.1109/CVPR.2011.5995544

. Birdal, T., Dobryden, I., Ilic, S. X-Tag: A Fiducial Tag for Flexible and Accurate Bundle Adjustment. (2016), pp. 556-564. http://dx.doi.org/10.1109/3DV.2016.65

. Burak, B., Cihan, T., Cuneyt, A. STag: A stable fiducial marker system, Image and Vision Computing, Vol 89, 2019, pp.158-169. https://doi.org/10.1016/j.imavis.2019.06.007

. Shingo, K., Hashimoto, K. Homography Estimation Using Marker Projection Control: A Case of Calibration-Free Projection Mapping, IFAC-Papers On Line, Vol 56, Issue 2, 2023, pp. 2951-2956. http://dx.doi.org/10.1016/j.ifacol.2023.10.1418

. Gonzalez, R. C., Woods, R. E. Digital image processing. Pearson Education limited, 4th Edition. (2017). https://doi.org/10.1117/1.3115362

. Suzuki, S., Be, K. , Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), (1985), 32–46. https://doi.org/10.1016/0734-189X(85)90016-7

. Douglas, D. H., & Peucker, T. K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: International Journal for Geographic Information and Geovisualization, 10(2), (1973), pp. 112–122. http://dx.doi.org/10.3138/FM57-6770-U75U-7727

. Li, Y., Zhu, S., Yu, Y., & Wang, Z. An improved graph-based visual localization system for indoor mobile robot using newly designed markers. International Journal of Advanced Robotic Systems, 15(2), (2018). https://doi.org/10.1177/1729881418769191

. Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), (1979), pp. 62–66. http://dx.doi.org/10.1109/21.35351

. Xuancen, L., Shifeng, Z., Jiayi, T., Longbin, L. An Onboard Vision-Based System for Autonomous Landing of a Low-Cost Quadrotor on a Novel Landing Pad, Sensors, (2019), 19, 4703. https://doi.org/10.3390/s19214703

. Acuna, R., Willert, V. Dynamic Markers: UAV Landing Proof of Concept, (2018) Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR), Workshop on Robotics in Education (WRE), Joao Pessoa, (2018), pp. 496-502. http://dx.doi.org/10.48550/arXiv.1709.04981

. Hartley, R., Zisserman, A. Multiple View Geomerty in Computer Vision. Cambridge University Press, second edition, 2003. https://doi.org/10.1017/s0263574700223217

. Vasileios, L., Panagiotis Minaidis, P., Lentaris, G., Dimitrios, S. Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC, Microprocessors and Microsystems, Volume 103, (2023). https://doi.org/10.1016/j.micpro.2023.104947

. Tourani, A., Bavle, H., Sanchez-Lopez, J. L., Salinas, R. M., Voos, H. Marker-Based Visual SLAM Leveraging Hierarchical Representations," 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 3461-3467. https://doi.org/10.1109/iros55552.2023.10341891




DOI: https://doi.org/10.31449/inf.v49i1.7259

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.