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A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment

Author

Listed:
  • Rachel Cohen

    (The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, University of Toronto, Toronto, ON M5G A2A, Canada)

  • Geoff Fernie

    (The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, University of Toronto, Toronto, ON M5G A2A, Canada
    Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada)

  • Atena Roshan Fekr

    (The Kite Research Institute, Toronto Rehabilitation Institute—University Health Network, University of Toronto, Toronto, ON M5G A2A, Canada
    Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada)

Abstract

Tripping hazards on the sidewalk cause many falls annually, and the inspection and repair of these hazards cost cities millions of dollars. Currently, there is not an efficient and cost-effective method to monitor the sidewalk to identify any possible tripping hazards. In this paper, a new portable device is proposed using an Intel RealSense D415 RGB-D camera to monitor the sidewalks, detect the hazards, and extract relevant features of the hazards. This paper first analyzes the effects of environmental factors contributing to the device’s error and compares different regression techniques to calibrate the camera. The Gaussian Process Regression models yielded the most accurate predictions with less than 0.09 mm Mean Absolute Errors (MAEs). In the second phase, a novel segmentation algorithm is proposed that combines the edge detection and region-growing techniques to detect the true tripping hazards. Different examples are provided to visualize the output results of the proposed method.

Suggested Citation

  • Rachel Cohen & Geoff Fernie & Atena Roshan Fekr, 2020. "A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8438-:d:445071
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    References listed on IDEAS

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    1. Zhong Qu & Fang-Rong Ju & Yang Guo & Ling Bai & Kuo Chen, 2018. "Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-14, July.
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