基于机器学习识别与标记分水岭分割的盲道图像定位

VTA (Visual Travel Aids) are devices used for addressing traveling difficulties of visually impaired people. To develop VTA for guiding visually impaired people to blind sidewalks, a method for blind sidewalk image location was presented based on machine learning recognition and a marked watershed algorithm. The algorithm located blind sidewalks by combining offline training with online recognition and segmentation. First, a blind sidewalk image was pretreated by converting an original image from a camera gradient view into an aerial view based on the plane equation of the blind sidewalk. The pretreating eliminates distortions. A Local Binary Pattern (LBP) descriptor then extracted the texture features of the aerial-view images. An offline cascade classifier trained through Adaboost recognized the blind sidewalk based on the LBP descriptor. The cascade classifier recognized the aerial view image online and roughly identified the blind area. The recognition results were then morphologically processed as markers to obtain the exact segmentation of the blind area through a marked watershed algorithm. Finally, the segmentation results were used to locate the centerline of the blind sidewalk. The algorithm was validated on the VTA. The experimental result showed that the blind sidewalk localization achieved 95.44% accuracy with an average speed of 8 frame/s. It exhibited a high accuracy rate while satisfying the real-time requirement, which are the necessary conditions for accurate 3D localization of blind sidewalks.