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Vanishing point detection for monocular camera extrinsic calibration under translation movement

© 2020 M. P. Abramov, O. S. Shipitko, A. S. Grigoryev, E. I. Ershov

Institute for Information Transmission Problems, Russian Academy of Sciences 127051 Moscow, Bolshoy Karetny per. 19, Russia
Moscow Institute of Physics and Technology (National Research University), 141701 Moscow Region, Dolgoprudny, Institutsky pereulok 9, Russia

Received 07 Oct 2019

In this paper we introduce a method for dynamic evaluation of pitch and yaw angles of a camera mounted on mobile unmanned vehicles (MUV). The proposed method is based on vanishing point detection from a single image under motion parallel to linear objects (buildings’ edges, road markup) located in the field of view. We demonstrated that rotation angles estimation mean absolute error for our method is 0.65°, which is comparable with state of the art dynamic extrinsic calibration methods. We conducted additional research for method applicability based on data recorded from a selfdriving car, which uses road markup for self-localization. It was shown that rotation angles evaluation error of 0.65° reduces localization accuracy by 0.01 m with a route length of about 70 m, which is acceptable for most applications.

Key words: dynamic calibration, extrinsic camera calibration, vanishing point, mobile unmanned vehicles, selfdriving car

DOI: 10.31857/S0235009220010023

Cite: Abramov M. P., Shipitko O. S., Grigoryev A. S., Ershov E. I. Poisk tochki skhoda dlya dinamicheskoi kalibrovki vneshnikh parametrov monokulyarnoi kamery pri uslovii pryamolineinogo dvizheniya [Vanishing point detection for monocular camera extrinsic calibration under translation movement]. Sensornye sistemy [Sensory systems]. 2020. V. 34(1). P. 32-43 (in Russian). doi: 10.31857/S0235009220010023


  • Abramov M.P., Shipitko O.S., Lukoyanov A.S., Panfilova E.I., Kunina I.A., Grigoryev A.S. Sistema pozitsionirovaniya vnutri zdanii mobil’noi robototekhnicheskoi platformy na osnove detektsii kraev [Edge detection based mobile robot indoor localization]. Sensornye sistemy [Sensory systems]. 2019. V. 33 (1). P. 30–43. DOI: 10.1134/S0235009219010025 (in Russian).
  • Ovchinkin A.A., Ershov E.I. Algoritm opredeleniya polozheniya puchka ehpipolyarnykh linii dlya sluchaya pryamolineinogo dvizheniya kamery [The algorithm of epipole position estimation under pure camera translation]. Sensornye sistemy [Sensory systems]. 2018. V. 32 (1). P. 42–49. DOI: 10.7868/S0235009218010079 (in Russian).
  • Forsyth D., Ponce J. Pinhole perspective. Computer Vision. A Modern Approach. Second edition. Pearson, 2012. С. 4–6.
  • Bell T., Xu J., Zhang S. Method for out-of-focus camera calibration. Applied optics. 2016. V. 55 (9). P. 2346–2352. https://doi.org/10.1364/AO.55.002346
  • Borji A. Vanishing point detection with convolutional neural networks. arXiv preprint arXiv:1609.00967. 2016.
  • Brady M.L. A fast discrete approximation algorithm for the radon transform. SIAM Journal on Computing. 1998. V. 27 (1). P. 107–119. https://doi.org/10.1137/S0097539793256673
  • Bui T.H., Nobuyama E., Saitoh T. A texture-based local soft voting method for vanishing point detection from a single road image. IEICE TRANSACTIONS on Information and Systems. 2013a. V. 96 (3). P. 690–698. https://doi.org/10.1587/transinf.E96.D.690
  • Bui T.H., Saitoh T., Nobuyama E. Road area detection based on texture orientations estimation and vanishing point detection. The SICE Annual Conference 2013. Nagoya. IEEE, 2013b. P. 1138–1143.
  • Canny J. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence. 1986. V. PAMI-8 (6). P. 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
  • Chaudhury K., DiVerdi S., Ioffe S. Auto-rectification of user photos. In 2014 IEEE International Conference on Image Processing (ICIP). Paris. IEEE, 2014. P. 3479–3483. https://doi.org/10.1109/ICIP.2014.7025706
  • Dubská M., Herout A., Havel J. PClines–line detection using parallel coordinates. In CVPR 2011. Colorado Springs. IEEE, 2011. P. 1489–1494. https://doi.org/10.1109/CVPR.2011.5995501
  • Fasano G., Accardo D., Tirri A.E., Moccia A., De Lellis E. Sky region obstacle detection and tracking for visionbased uas sense and avoid. Journal of Intelligent & Robotic Systems. 2016. V. 84 (1–4). P. 121–144. https://doi.org/10.1007/s10846-015-0285-0
  • Godha S. On-road obstacle detection system for driver assistance. Asia Pacific Journal of Engineering Science and Technology. 2017. V. 3 (1). P. 16–21.
  • Harris C.G., Stephens M. A combined corner and edge detector. Alvey vision conference. 1988. V. 15. P. 10–52.
  • Kunina I.A., Teplyakov L.M., Gladkov A.P., Khanipov T.M., Nikolaev D.P. Aerial images visual localization on a vector map using color-texture segmentation. Tenth International Conference on Machine Vision (ICMV 2017). Viena. International Society for Optics and Photonics, 2018. V. 10696. P. 106961T. https://doi.org/10.1117/12.2310138.
  • Lee T.S. Image representation using 2d gabor wavelets. IEEE Transactions on pattern analysis and machine intelligence. 1996. V. 18 (10). P. 959–971. https://doi.org/10.1109/34.541406
  • Lezama J., Grompone von Gioi R., Randall G., Morel J.-M. Finding vanishing points via point alignments in image primal and dual domains. Proceedings of the IEEE Conference on Computer
  • Lu X., Yaoy J., Li H., Liu Y. 2-line exhaustive searching for real-time vanishing point estimation in manhattan world. Winter Conference on Applications of Computer Vision (WACV). Santa Rosa. IEEE, 2017. P. 345–353. https://doi.org/10.1109/WACV.2017.45.
  • Mur-Artal R., Montiel J.M.M., Tardos J.D. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics. 2015. V. 31 (5). P. 1147–1163. https://doi.org/10.1109/TRO.2015.2463671
  • Nikolaev D.P., Karpenko S.M., Nikolaev I.P., Nikolayev P.P. Hough transform: underestimated tool in the computer vision field. Proceedings of the 22th European Conference on Modelling and Simulation. 2008. V. 238. P. 246.
  • Nistér D., Naroditsky O., Bergen J. Visual odometry. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. Washington. IEEE, 2004. V. 1. P. 1–10. https://doi.org/10.1109/CVPR.2004.1315094.
  • Rasmussen C. Grouping dominant orientations for illstructured road following. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. Washington. IEEE, 2004. V. 1. P. 1–9. https://doi.org/10.1109/CVPR.2004.1315069.
  • Stockman G., Shapiro L.G. Computer Vision. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2001. 196 p." - "Von Gioi R.G., Jakubowicz J., Morel J.-M., Randall G. Lsd: a line segment detector. Image Processing On Line. 2012. V. 2. P. 35–55. https://doi.org/10.5201/ipol.2012.gjmr-lsd
  • Wang Y., Dahnoun N., Achim A. A novel system for robust lane detection and tracking. Signal Processing. 2012. V. 92 (2). P. 319–334. https://doi.org/10.1016/j.sigpro.2011.07.019
  • Wilcox R.R. Introduction to robust estimation and hypothesis testing. Academic press. 2011. 690 p.