Pedestrian lane detection in unstructured environments for assistive navigation
Automatically finding paths is a crucial and challenging task in autonomous navigation systems. The task becomes more difficult in unstructured environments such as indoor or outdoor scenes with unmarked pedestrian lanes under severe illumination conditions, complex lane surface structures, and occlusion. This paper proposes a robust method for pedestrian lane detection in such unstructured environments. The proposed method detects the walking lane in a probabilistic framework integrating both appearance of the lane region and characteristics of the lane borders. The vanishing point is employed to identify the lane borders. We propose an improved vanishing point estimation method based on orientation of color edges, and use pedestrian detection for occlusion handling. The proposed pedestrian lane detection method is evaluated on a new data set of 2000 images collected from various indoor and outdoor scenes with different types of unmarked lanes. Experimental results and comparisons with other existing methods on the new data set have shown the efficiency and robustness of the proposed method.
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