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Ellipse Detection for Visual Cyclists Analysis “In the Wild”

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10424))

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Abstract

Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.

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References

  1. Ardeshiri, T., Larsson, F., Gustafsson, F., Schön, T.B., Felsberg, M.: Bicycle tracking using ellipse extraction. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)

    Google Scholar 

  2. Basca, C., Talos, M., Brad, R.: Randomized hough transform for ellipse detection with result clustering. In: The International Conference on Computer as a Tool, EUROCON 2005, vol. 2, pp. 1397–1400. IEEE (2005)

    Google Scholar 

  3. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  4. Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 26–33 (2005)

    Google Scholar 

  5. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  6. Chia, A.Y.S., Leung, M.K., Eng, H.-L., Rahardja, S.: Ellipse detection with hough transform in one dimensional parametric space. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 5, p. V-333 (2007)

    Google Scholar 

  7. Cooke, T.: A fast automatic ellipse detector. In: 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 575–580. IEEE (2010)

    Google Scholar 

  8. Duan, F., Wang, L., Guo, P.: RANSAC based ellipse detection with application to catadioptric camera calibration. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010. LNCS, vol. 6444, pp. 525–532. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17534-3_65

    Chapter  Google Scholar 

  9. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  10. Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476–480 (1999)

    Article  Google Scholar 

  11. Fornaciari, M., Prati, A., Cucchiara, R.: A fast and effective ellipse detector for embedded vision applications. Pattern Recogn. 47(11), 3693–3708 (2014)

    Article  Google Scholar 

  12. Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graphics (TOG) 25(1), 130–150 (2006)

    Article  Google Scholar 

  13. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  14. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)

    Google Scholar 

  15. Huang, Y.-H., Pan, B.-C., Zheng, S.-L., Pan, J., Tang, Y.: Lip-reading detection and localization based on two stage ellipse fitting. In: International Conference Wavelet Analysis and Pattern Recognition. IEEE (2008)

    Google Scholar 

  16. Jähne, B.: Digital Image Processing (2002)

    Google Scholar 

  17. Kaewapichai, W., Kaewtrakulpong, P.: Robust ellipse detection by fitting randomly selected edge patches

    Google Scholar 

  18. Kovesi, P.: Edge linking and line segment fitting

    Google Scholar 

  19. Larsson, F., Felsberg, M., Forssen, P.-E.: Correlating fourier descriptors of local patches for road sign recognition. IET Comput. Vision 5(4), 244–254 (2011)

    Article  MathSciNet  Google Scholar 

  20. Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., Gavrila, D.M.: A new benchmark for vision-based cyclist detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 1028–1033. IEEE (2016)

    Google Scholar 

  21. Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 286–299 (2007)

    Article  Google Scholar 

  22. Liu, H., Ran, B.: Vision-based stop sign detection and recognition system for intelligent vehicles. Transp. Res. Rec. J. Transp. Res. Board 1748, 161–166 (2001)

    Article  Google Scholar 

  23. McLaughlin, R.A.: Randomized hough transform: improved ellipse detection with comparison. Pattern Recogn. Lett. 19(3), 299–305 (1998)

    Article  MATH  Google Scholar 

  24. Mori, G., Malik, J.: Recognizing objects in adversarial clutter: breaking a visual captcha. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, vol. 1, p. I. IEEE (2003)

    Google Scholar 

  25. Opelt, A., Pinz, A., Zisserman, A.: A boundary-fragment-model for object detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006). doi:10.1007/11744047_44

    Chapter  Google Scholar 

  26. Prasad, D.K., Leung, M.K., Cho, S.-Y.: Edge curvature and convexity based ellipse detection method. Pattern Recogn. 45(9), 3204–3221 (2012)

    Article  Google Scholar 

  27. Pu, J., Zheng, B., Leader, J.K., Gur, D.: An ellipse-fitting based method for efficient registration of breast masses on two mammographic views. Med. Phys. 35(2), 487–494 (2008)

    Article  Google Scholar 

  28. Radim Halir, J.F.: Numerically stable direct least squares fitting of ellipses (1998)

    Google Scholar 

  29. Rocha, J., Pavlidis, T.: A shape analysis model with applications to a character recognition system. IEEE Trans. Pattern Anal. Mach. Intell. 16(4), 393–404 (1994)

    Article  Google Scholar 

  30. Takegami, T., Gotoh, T., Ohyama, G.: An algorithm for model-based stable pupil detection for eye tracking system. Syst. Comput. Japan 35(13), 21–31 (2004)

    Article  Google Scholar 

  31. Teutsch, C., Berndt, D., Trostmann, E., Weber, M.: Real-time detection of elliptic shapes for automated object recognition and object tracking. In: Electronic Imaging 2006, p. 60700J. International Society for Optics and Photonics (2006)

    Google Scholar 

  32. Xie, Y., Ji, Q.: A new efficient ellipse detection method. In: 16th International Conference on Pattern Recognition, Proceedings, vol. 2, pp. 957–960 (2002)

    Google Scholar 

  33. Xu, L., Oja, E., Kultanen, P.: A new curve detection method: randomized hough transform (RHT). Pattern Recogn. Lett. 11(5), 331–338 (1990)

    Article  MATH  Google Scholar 

  34. Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., Sick, B.: Trajectory prediction of cyclists using a physical model and an artificial neural network. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 833–838. IEEE (2016)

    Google Scholar 

  35. Åström, K.J., Klein, R.E., Lennartsson, A.: Bicycle Dyn. Control 25(4), 26–47 (2005)

    Google Scholar 

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Acknowledgments

This work has been supported by VR (EMC\(^{2}\), ELLIIT, starting grant [2016-05543]) and Vinnova (Cykla).

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Authors and Affiliations

  1. Computer Vision Laboratory, Linköping University, Linköping, Sweden

    Abdelrahman Eldesokey, Michael Felsberg & Fahad Shahbaz Khan

Authors
  1. Abdelrahman Eldesokey
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  2. Michael Felsberg
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  3. Fahad Shahbaz Khan
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Corresponding author

Correspondence to Abdelrahman Eldesokey .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

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Eldesokey, A., Felsberg, M., Khan, F.S. (2017). Ellipse Detection for Visual Cyclists Analysis “In the Wild”. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-64689-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64688-6

  • Online ISBN: 978-3-319-64689-3

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