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Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint

Received: 12 March 2015    Accepted: 24 March 2015    Published: 30 March 2015
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Abstract

A lane detection method of statistical Hough transform based on gradient constraint is proposed to solve the problem of computational cost and grid quantization precision of classical Hough transform. Statistical Hough transform uses the Gaussian kernel function to model each pixel in the image .The size of initial data set is limited by using the method of gradient constraint. Eventually lane parameters’ continuous probability density function is given. The results of the experimentation show that under highway circumstance the provided method can rapidly and robustly detect the lane.

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 2)
DOI 10.11648/j.ijiis.20150402.12
Page(s) 40-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Statistical Hough Transform, Gaussian Kernel Function, Gradient Constraint

References
[1] Aharon Bar Hillel, Ronen Lerner, Danlevi, Guy Raz. “Recent progress in road and lane detection: a survey”, Machine Vision and Application, vol. 10, no. 4, pp.727-745, 2014.
[2] Rozenn Dahyot, “Statistical Hough transform”, IEEE Transactions on pattern analysis and machine intelligence, vol. 31, no.8, pp.1502-1509, 2009.
[3] G.Liu, F.Wörgötter, I.Markelic´, “combining statistical Hough transform and particle filter for rubust lane detection and tracking”, In Proceeding(s) of IEEE Intelligent Vehicles Symposinm University of California,pp.993-997,2010
[4] G.Liu, F.Wörgötter, I.arkeli, “Stochastic lane shape estimation using local image descriptors”, IEEE Transactions on intelligent transportation systems, IEEE, vol. 14, no. 1, pp.13-21, 2013.
[5] H.Yoo, U.Yang, K.Sohn, “Gradient-enhancing conversion for illumination-robust lane detection”, IEEE Transactions on intelligent transportation systems, IEEE, vol. 14, no.3, pp.1083-1094, 2013.
[6] R.Gopalan,T.Hong,M.Shneier. “A learning approach towards detection and tracking of lane markings”, IEEE Transactions on intelligent transportation systems, IEEE, vol. 13, no.3, pp.1088-1098, 2012.
[7] Z.Kim. “Robust lane detection and tracking in challenging scenarios”, IEEE Transactions on intelligent transportation systems, IEEE, vol. 9, no.1, pp.16-26, 2008.
[8] Amol Borkar, Monson Hayes,Mark T.Smith. “A novel lane detection system with efficient ground truth generation”, IEEE Transactions on intelligent transportation systems, IEEE, vol.13, no.1, pp.365-374, 2012.
[9] J.C.McCall, M.M.Triedi, “Video based lane estimation and tracking for driver assistance:Survey,system and evaluation”. IEEE Transactions on intelligent transportation systems, IEEE, vol.7, no.1, pp.20-37, 2006.
[10] Guangtao Cui, Junzheng Wang, Jing Li, “Robust multilane detection and tracking in urban scenarios based on LIDAR and mono-vision ”, IET Image Processing, vol.8, no.5, pp.269-279, 2014.
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Cite This Article
  • APA Style

    Peng Yan-zhou, Gao Hong-feng. (2015). Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint. International Journal of Intelligent Information Systems, 4(2), 40-45. https://doi.org/10.11648/j.ijiis.20150402.12

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    ACS Style

    Peng Yan-zhou; Gao Hong-feng. Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint. Int. J. Intell. Inf. Syst. 2015, 4(2), 40-45. doi: 10.11648/j.ijiis.20150402.12

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    AMA Style

    Peng Yan-zhou, Gao Hong-feng. Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint. Int J Intell Inf Syst. 2015;4(2):40-45. doi: 10.11648/j.ijiis.20150402.12

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  • @article{10.11648/j.ijiis.20150402.12,
      author = {Peng Yan-zhou and Gao Hong-feng},
      title = {Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint},
      journal = {International Journal of Intelligent Information Systems},
      volume = {4},
      number = {2},
      pages = {40-45},
      doi = {10.11648/j.ijiis.20150402.12},
      url = {https://doi.org/10.11648/j.ijiis.20150402.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20150402.12},
      abstract = {A lane detection method of statistical Hough transform based on gradient constraint is proposed to solve the problem of computational cost and grid quantization precision of classical Hough transform. Statistical Hough transform uses the Gaussian kernel function to model each pixel in the image .The size of initial data set is limited by using the method of gradient constraint. Eventually lane parameters’ continuous probability density function is given. The results of the experimentation show that under highway circumstance the provided method can rapidly and robustly detect the lane.},
     year = {2015}
    }
    

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    T1  - Lane Detection Method of Statistical Hough Transform Based on Gradient Constraint
    AU  - Peng Yan-zhou
    AU  - Gao Hong-feng
    Y1  - 2015/03/30
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    N1  - https://doi.org/10.11648/j.ijiis.20150402.12
    DO  - 10.11648/j.ijiis.20150402.12
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 40
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.20150402.12
    AB  - A lane detection method of statistical Hough transform based on gradient constraint is proposed to solve the problem of computational cost and grid quantization precision of classical Hough transform. Statistical Hough transform uses the Gaussian kernel function to model each pixel in the image .The size of initial data set is limited by using the method of gradient constraint. Eventually lane parameters’ continuous probability density function is given. The results of the experimentation show that under highway circumstance the provided method can rapidly and robustly detect the lane.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • College of Information Engineering, Henan University of Science and Technology, Luoyang, China

  • College of Information Engineering, Henan University of Science and Technology, Luoyang, China

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