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A robust vision-based method for staircase detection and localization

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Abstract

A robust vision-based staircase identification method is proposed, which comprises 2D staircase detection and 3D staircase localization. The 2D detector pre-screens the input image, and the 3D localization algorithm continues the task of retrieving geometry of the staircase on the reported region in the image. A novel set of principal component analysis-based Haar-like features are introduced, which extends the classical Haar-like features from local to global domain and are extremely efficient at rejecting non-object regions for the early stages of the cascade, and the Viola–Jones rapid object detection framework is improved to adapt the context of staircase detection, modifications have been made on the scanning scheme, multiple detections integrating scheme and the final detection evaluation metrics. The V-disparity concept is applied to detect the planar regions on the staircase surface and locate 3D planes quickly from disparity maps, and then, the 3D position of staircase is localized robustly. Finally, experiments show the performance of the proposed method.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61203332) and the Natural Science Foundation of Jiangsu (SBK201321351).

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215006, China

    Li Maohai & Sun Lining

  2. School of Mechanical and Aerospace of Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    Li Maohai

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    Wang Han

  4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Cai Zesu

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  1. Li Maohai
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  2. Wang Han
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  3. Sun Lining
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  4. Cai Zesu
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Correspondence to Li Maohai.

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Maohai, L., Han, W., Lining, S. et al. A robust vision-based method for staircase detection and localization. Cogn Process 15, 173–194 (2014). https://doi.org/10.1007/s10339-013-0588-7

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  • DOI: https://doi.org/10.1007/s10339-013-0588-7

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