Authors:Viviana Weiss1;Séverine Cloix2;Guido Bologna1;David Hasler3 andThierry Pun1
Affiliations:1University of Geneva, Switzerland;2CSEM SA and University of Geneva, Switzerland;3CSEM SA, Switzerland
Keyword(s):Ground Change Detection, Colour and Texture Segmentation, Local Edge Patterns (LEP), Artificial Neural Network (ANN), Elderly Care, Gerontechnology.
RelatedOntology Subjects/Areas/Topics:Applications and Services ;Color and Texture Analyses ;Computer Vision, Visualization and Computer Graphics ;Enterprise Information Systems ;Features Extraction ;Human and Computer Interaction ;Human-Computer Interaction ;Image and Video Analysis
Abstract:Nowadays, there are many different types of mobility aids for elderly people. Nevertheless, these devices may lead to accidents, depending on the terrain where they are being used. In this paper, we present a robust ground change detector that will warn the user of potentially risky situations. Specifically, we propose a robust classification algorithm to detect ground changes based on colour histograms and texture descriptors. In our design, we compare the current frame and the average of the k previous frames using different colour systems and Local Edge Patterns. To assess the performance of our algorithm, we evaluated different Artificial Neural Networks architectures. The best results were obtained by representing in the input neurons measures related to Histogram Intersections, Kolmogorov-Smirnov distance, Cumulative Integrals and Earth mover’s distance. Under real environmental conditions our results indicated that our proposed detector can accurately distinguish the grounds changes in real-time.(More)
Nowadays, there are many different types of mobility aids for elderly people. Nevertheless, these devices may lead to accidents, depending on the terrain where they are being used. In this paper, we present a robust ground change detector that will warn the user of potentially risky situations. Specifically, we propose a robust classification algorithm to detect ground changes based on colour histograms and texture descriptors. In our design, we compare the current frame and the average of the k previous frames using different colour systems and Local Edge Patterns. To assess the performance of our algorithm, we evaluated different Artificial Neural Networks architectures. The best results were obtained by representing in the input neurons measures related to Histogram Intersections, Kolmogorov-Smirnov distance, Cumulative Integrals and Earth mover’s distance. Under real environmental conditions our results indicated that our proposed detector can accurately distinguish the grounds changes in real-time.