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Geometrical Feature Based Stairways Detection and Recognition using Depth Sensor

机译：基于几何特征的楼梯检测和识别使用深度传感器

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Stairways detection and distance measurement have been a continuous challenge of research area in human-system interaction to reach topnotch solution with greater portability in assisting visually impaired people and guiding autonomous navigation system at smart environments in the real world. For that, a framework is proposed in this work to detect the stair region from depth stair image based on a unique geometrical feature of a stair. The unique geometrical feature is every stair step's height gradually decreases from bottom to top of the stair. For that initially, the depth image is preprocessed and extracted the Canny edge image. After that, a proposed edge linking procedure is utilized through the Brute-Force Search technique to improve the broken edges. Furthermore, a non-candidate edge elimination procedure is used to extract the longest potential concurrent horizontal edge segment by considering the orientation of the horizontal edges. Finally, the extracted potential concurrent horizontal edge segment is verified as stair edge segment by justifying the aforementioned unique feature of stair and detects the stair region of interest (ROI). Furthermore, one-dimensional depth feature is extracted from the ROI and sent to the support vector machine (SVM) for recognizing the up, down, and negative stair. The distance of the recognized stair region from the camera is estimated based on the depth feature. Stairs images captured under different lighting conditions have been used to test the proposed framework to evaluate the resultant accuracy of the system.
机译：楼梯检测和距离测量一直是人力系统相互作用中的研究领域的持续挑战，以实现更大的便携性，在辅助视障人士和在现实世界中的智能环境中引导自主导航系统的更大便携性。为此，在这项工作中提出了一种框架，以根据楼梯的独特几何特征来检测从深度楼梯图像中的楼梯区。独特的几何特征是每个台阶的高度逐渐从楼梯的底部逐渐减小。例如，预处理深度图像并提取Canny Edge图像。之后，通过布鲁力搜索技术利用所提出的边缘连接过程来改善破碎的边缘。此外，非候选边缘消除过程用于通过考虑水平边缘的方向来提取最长潜在的并发水平边缘段。最后，提取的潜在的并发水平边缘段通过证明上述楼梯的独特特征验证为阶梯边缘段，并检测感兴趣的楼梯区域（ROI）。此外，从ROI中提取一维深度特征，并发送到支持向量机（SVM），以识别UP，DOWN和负阶梯。基于深度特征估计识别的楼梯区与相机的距离。在不同的照明条件下捕获的楼梯图像已被用于测试所提出的框架，以评估系统的结果精度。

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