Depth-aware indoor staircase detection and recognition for the visually impaired

A mobile vision-based navigation aid is capable of assisting the visually impaired to travel independently, especially in unfamiliar environments. Despite many effective navigation algorithms having been developed in recent decades, accurate, efficient, and reliable staircase detection in indoor navigation still remains to be a challenging problem. In this paper, we propose an effective indoor staircase detection algorithm based on an RGB-D camera. The candidates of staircases are first detected from RGB frames by extracting a set of concurrent parallel lines based on Hough transform. The complement depth frames are further employed to recognize the staircase candidates as upstairs, downstairs, and negatives (i.e., corridors). A support vector machine (SVM) based multi-classifier is trained and tested for the staircase recognition with our newly collected staircase dataset. The detection and recognition results demonstrate the effectiveness and efficiency of the proposed algorithm.

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