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.
Int J Environ Res Public Health. 2020 Nov 14;17(22):8438. doi: 10.3390/ijerph17228438.
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.
人行道上的绊倒危险每年都会导致许多人摔倒，而检查和修复这些危险会花费城市数百万美元。目前，还没有一种高效且具有成本效益的方法来监测人行道以识别任何可能的绊倒危险。在本文中，提出了一种新的便携式设备，该设备使用英特尔 RealSense D415 RGB-D 相机来监测人行道，检测危险，并提取危险的相关特征。本文首先分析了导致设备误差的环境因素的影响，并比较了不同的回归技术来校准相机。高斯过程回归模型产生了最准确的预测，平均绝对误差 (MAE) 小于 0.09 毫米。在第二阶段，提出了一种新的分割算法，该算法结合了边缘检测和区域生长技术来检测真正的绊倒危险。提供了不同的示例来可视化所提出方法的输出结果。
