Real time static/dynamic obstacle detection for visually impaired persons

In this paper we introduce a novel framework for detecting static/moving obstacles in order to assist visually impaired/blind persons to navigate safely. Firstly, a set of interest points is extracted base on an image grid and tracked using the multiscale Lucas - Kanade algorithm. Next, the camera/background motion is determined through a set of homographic transforms, estimated by recursively applying the RANSAC algorithm on the interest point correspondence while other types of movements are identified using an agglomerative clustering technique. Finally, obstacles are classified as urgent/normal based on their distance to the subject and motion vectors orientation. The experimental results performed on various challenging scenes demonstrate that our approach is effective in videos with important camera movement, including noise and low resolution data.