Abstract
Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This whole algorithm includes a checkerboard corner detection network and some post-processing techniques. The network model is a fully convolutional network with improvements of loss function and learning rate, which can deal with the images of arbitrary size and produce correspondingly-sized output with a corner score on each pixel by efficient inference and learning. Besides, in order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering. Evaluations on two different datasets show its superior robustness, accuracy and wide applicability in quantitative comparisons with the state-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)
Heikkila, J.: Geometric camera calibration using circular control points. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1066–1077 (2000)
Fiala, M., Shu, C.: Self-identifying patterns for plane-based camera calibration. Mach. Vis. Appl. 19(4), 209–216 (2008)
Mallon, J., Whelan, P.F.: Which pattern? Biasing aspects of planar calibration patterns and detection methods. Pattern Recognit. Lett. 28(8), 921–930 (2007)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, vol. 15, pp. 147–151 (1988)
Smith, S.M., Brady, J.M.: SUSAN—a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)
Su, J., Duan, X., Xiao, J.: Fast detection method of checkerboard corners based on the combination of template matching and Harris Operator. In: International Conference on Information Science and Technology (ICIST) 2013, pp. 858–861. IEEE (2013)
Zhu, W., et al.: A fast and accurate algorithm for chessboard corner detection. In: Proceedings of the IEEE 2nd International Congress on Image and Signal Processing (CISP 2009), Tianjin, China, pp. 1–5 (2009)
Wang, Z., et al.: Recognition and location of the internal corners of planar checkerboard calibration pattern image. Appl. Math. Comput. 185(2), 894–906 (2007)
Bennett, S., Lasenby, J.: ChESS—Quick and robust detection of chess-board features. Comput. Vis. Image Underst. 118, 197–210 (2014)
Vezhnevets, V.: OpenCV calibration object detection. Part of the Free Open-Source OpenCV Image Processing Library (2016)
Rufli, M., Scaramuzza, D., Siegwart, R.: Automatic detection of checkerboards on blurred and distorted images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, IROS 2008, pp. 3121–3126. IEEE (2008)
Placht, S., Fürsattel, P., Mengue, E.A., Hofmann, H., Schaller, C., Balda, M., Angelopoulou, E.: ROCHADE: robust checkerboard advanced detection for camera calibration. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 766–779. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_50
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_34
Rosten, E., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)
Donné, S., et al.: MATE: Machine learning for adaptive calibration template detection. Sensors 16(11), 1858 (2016)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of 27th International Conference on Machine Learning, pp. 807–814 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Sermanet, P., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: ICLR (2014)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. OSDI 16, 265–283 (2016)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)
Lucchese, L., Mitra, S. K.: Using saddle points for subpixel feature detection in camera calibration targets. In: Proceedings of the IEEE 2002 Asia-Pacific Conference on Circuits and Systems, Bali, Indonesia, pp. 191–195 (2002)
Acknowledgement
This work is partially supported by the National Natural Science Foundation of China (Grant No. 51335004 and No. 91648203) and the International Science & Technology Cooperation Program of China (Grant No. 2016YFE0113600).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, B., Xiong, C., Zhang, Q. (2018). CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-97589-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97588-7
Online ISBN: 978-3-319-97589-4
eBook Packages: Computer ScienceComputer Science (R0)