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Efficient railway tracks detection and turnouts recognition method using HOG features

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Abstract

Railway tracks detection and turnouts recognition are the basic tasks in driver assistance systems, which can determine the interesting regions for detecting obstacles and signals. In this paper, a novel railway tracks detection and turnouts recognition method using HOG (Histogram of Oriented Gradients) features was presented. At first, the approach computes HOG features and establishes integral images, and then extracts railway tracks by region-growing algorithm. Then based on recognizing the open direction of the turnout, we find the path where the train will travel through. Experiments demonstrated that our method was able to correctly extract tracks and recognize turnouts even in very bad illumination conditions and run fast enough for practical use. In addition, our approach only needs a computer and a cheap camera installed in the railroad vehicle, not specialized hardwares and equipment.

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Notes

  1. If only finding one railway track, the other can be obtained by mirror-method. We may delete extra tracks by mirror-method and information of image sequences if more than two railway tracks are detected.

  2. If the change of the distance between two railway tracks is monotonic, only the half of one railway track is detected and connected with tapering rail (In practice, this case is almost impossible to happen, we may deal with it as no existing turnout).

  3. s 1 is the maximum lateral deviation s 1 of the center X t of the currently traveled track from the center of the vehicle.

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Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (No.709- 21061, No.10601064, No.61153003), the CAS/SAFEA International Partnership Program for Creative Research Teams and Major International (Ragional) Joint Research Project (No.71110107026), the President Fund of GUCAS, and the National Technology Support Program 2009BAH42B02.

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Authors and Affiliations

  1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiquan Qi, Yingjie Tian & Yong Shi

  2. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Yong Shi

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  1. Zhiquan Qi
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  2. Yingjie Tian
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  3. Yong Shi
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Correspondence to Yingjie Tian or Yong Shi.

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Qi, Z., Tian, Y. & Shi, Y. Efficient railway tracks detection and turnouts recognition method using HOG features. Neural Comput & Applic 23, 245–254 (2013). https://doi.org/10.1007/s00521-012-0846-0

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