Context-aware obstacle detection for navigation by visually impaired

Abstract

This paper presents a context-aware smartphone-based based visual obstacle detection approach to aid visually impaired people in navigating indoor environments. The approach is based on processing two consecutive frames (images), computing optical flow, and tracking certain points to detect obstacles. The frame rate of the video stream is determined using a context-aware data fusion technique for the sensors on smartphones. Through an efficient and novel algorithm, a point dataset on each consecutive frames is designed and evaluated to check whether the points belong to an obstacle. In addition to determining the points based on the texture in each frame, our algorithm also considers the heading of user movement to find critical areas on the image plane. We validated the algorithm through experiments by comparing it against two comparable algorithms. The experiments were conducted in different indoor settings and the results based on precision, recall, accuracy, and f-measure were compared and analyzed. The results show that, in comparison to the other two widely used algorithms for this process, our algorithm is more precise. We also considered time-to-contact parameter for clustering the points and presented the improvement of the performance of clustering by using this parameter.