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Compared to other disabilities, people with visual impairment are more inconvenient to take care themselves. However, the development in blindness guidance aids is slow, for example, white cane has been used for more than a hundred years, but there appears no new aids can completely replace it. Guide dogs are another option, but it is not easy to use widely. Therefore, more and more researches of aid system using computer vision have been published.
In this paper, we use Kinect as the depth sensor to build a guidance aid system for blinds. The system has two features. (1) Rebuild the path information basing on the normal vector provided by Kinect with the algorithm of erosion and dilation, which could retrieve the length of road and the height of obstacle (if any), helping the visually impaired to recognize the walking path. (2) Provide the assistance for the visually impaired to find daily necessities. First, our system trains identification model from convolutional neural networks, which will finally applied for the recognition in a series of segmented images, and get the item locations from the statistical results. That could help the visually impaired quickly find those items.
The accuracy of the floor extraction algorithm used in the system is about 98.8%, which means it could fix the errors read from Kinect in the depth information and could efficiently extraction the regions belong to the floor. For the object detection tests, we prepared ten thousand pictures for each of the three conditions, and we got only less than 5% of chances not to find the target object, which certainly proves that the method we used could efficiently identify the target object. | en_US |