Geometrical Feature Based Stairways Detection and Recognition Using Depth Sensor

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.