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

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 quite mandatory. Low cost systems suggest use of poor quality cameras. However, rea...

Full description

Saved in:
Bibliographic Details
Published in:Electronic letters on computer vision and image analysis 2016-08, Vol.15 (1), p.60-76
Main Authors: Frikha, Jihen, Sellami, Dorra, Khanfir Kallel, Imen
Format: Article
Language:English
Subjects:
Computer Science
Computer Vision
Engineering Sciences
fusion
Image Analysis Processing
Image Processing
image segmentation
indoor
indoor/outdoor Environment
outdoor Environment
possibility theory
reference window
Signal and Image processing
Traversable area segmentation
visually impaired people
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 quite mandatory. Low cost systems suggest use of poor quality cameras. However, real time 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, indoor and outdoor environments add additional variability to traversable areas. In this paper, we present a real-time approach for fast traversable area segmentation 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, with indoor and outdoor environments. Experimental results show that this method is challenging leading to higher segmentation rates.
ISSN:1577-5097
1577-5097
DOI:10.5565/rev/elcvia.818