Indoor/outdoor navigation system based on possibilistic traversable area segmentation for visually impaired people

Abstract

Autonomous collision avoidance for visually impaired people requires a specific processing for an accurate definition of traversable area. Processing of a real time image sequence for traversable area segmentation is mandatory. Low cost systems suggest use of poor quality cameras. However, low cost camera suffers from great variability of traversable area appearance at indoor as well as outdoor environments. Taking into account ambiguity affecting object and traversable area appearance induced by reflections, illumination variations, occlusions (, etc...), an accurate segmentation of traversable area in such conditions remains a challenge. Moreover, at indoor and outdoor environments, more added variability is induced. In this paper, we present a fast traversable area segmentation approach from image sequence recorded by a low-cost monocular camera for navigation system. Taking into account all kinds of variability in the image, we apply possibility theory for modeling information ambiguity. An efficient way of updating the traversable area model in each environment condition is to consider traversable area samples from the same processed image for building its possibility maps. Then fusing these maps allows making a fair model definition of the traversable area. Performance of the proposed system was evaluated on public databases, at indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates.

Detecting Traversable Area and Water Hazards for the Visually Impaired with a pRGB-D Sensor

“…Badino proposed to represent the 3D situation with a set of rectangular sticks for autonomous systems by taking into account the fact that the traversable area in front of vehicles is limited by objects with almost vertical surfaces and estimated the road by fitting a B-spline surface instead of assuming a planar road [ 43 , 44 ]. Elleuch developed a segmentation approach based on possibility modeling theory by imposing a crucial starting point, namely considering reference area placed in the bottom of the image to be traversable [ 45 , 46 ]. As for estimating the surface normal vector to determine traversable areas, Koester detected accessible sections by calculating the gradients and estimating the surface normal vector directions of real-world scene patches.…”

Uncertainty Theories Based Iris Recognition System

“…Zadeh [14] was the first to introduce this theory and, subsequently, it was improved by several other authors, like Dubois and Prade [13], Cooman and Aeyels [15] among others. This theory has developed rapidly in recent years and has been widely used in various fields such as, diagnosis [16], obstacle avoidance systems and electronic travel aids (ETA) [17], and biometrics [18], [19].…”