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Visual railway detection by superpixel based intracellular decisions

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Abstract

Safety is one of the most concerned issues in traffic and transportation, among which railway detection is a fundamental and necessary research. In this paper, we propose a visual railway detection method based on superpixels rather than pixels. An SVM classifier is learned based on features, on which a TF-IDF like transform is applied, and it greatly improves the performance of the classification. The intracellular decision scheme is proposed to make decisions on a superpixel by using predictions of features within the superpixel. All the superpixels that are predicted as positive constitute the railway to be detected. The proposed railway detection method is evaluated on a number of challenging images and experiments demonstrate that the proposed method is an effective and detailed solution to railway detection, and is superior to other railway detection methods.

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Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities with grant number 2014JBM040.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Zhu Teng, Feng Liu & Baopeng Zhang

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  1. Zhu Teng
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  2. Feng Liu
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  3. Baopeng Zhang
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Correspondence to Zhu Teng.

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Teng, Z., Liu, F. & Zhang, B. Visual railway detection by superpixel based intracellular decisions. Multimed Tools Appl 75, 2473–2486 (2016). https://doi.org/10.1007/s11042-015-2654-x

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  • DOI: https://doi.org/10.1007/s11042-015-2654-x

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