Traversable area segmentation approach at indoor environment for visually impaired people

For a safe navigation of visually impaired people, traversable area segmentation seems well important for preventing collision or falling down. In this respect, reducing the system cost entails applying low cost low resolution monocular cameras, leading to poor quality images. Adding potential navigation associated camera vibration, the system proves to be liable to some data imperfections. Such image affecting imperfections refer well to the necessity of applying the possibility theory as an appropriate means for reducing information ambiguity. In this regard, a new traversable area segmentation based on possibility modeling theory approach is being developed in this work. Noteworthy and for satisfactory adaptation of our model to image condition variability, a crucial starting point is imposed, namely considering reference area placed in the bottom of the image, assumed to be a traversable area. Actually, the proposed approach involves three major steps, namely: a color feature extraction step, a possibility distribution generation step, based on Dubois and Prade principle, as well as a data fusion step where a conjunctive operator is achieved for generating a distinctive unique possibility map. To note, the proposed approach is validated on a publicly as well as a developed databases. A mean accuracy rate of 98% is obtained with respect to both databases.