Finding objects for assisting blind people

Computer vision technology has been widely used for blind assistance, such as navigation and wayfinding. However, few camera-based systems are developed for helping blind or visually-impaired people to find daily necessities. In this paper, we propose a prototype system of blind-assistant object finding by camera-based network and matching-based recognition. We collect a dataset of daily necessities and apply Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) feature descriptors to perform object recognition. Experimental results demonstrate the effectiveness of our prototype system.