Title: An automatic natural feature selection system for indoor tracking - application to Alzheimer patient support

Authors: Mohamed Badeche; Frédéric Bousefsaf; Abdelhak Moussaoui; Mohamed Benmohammed; Alain Pruski

Addresses: Lire Laboratory, Constantine 2 - Abdelhamid Mehri University, BP 325, Route Ain El Bey, 25017, Constantine, Algeria ' Vision and Content Engineering Laboratory - CEA LIST, CEA SACLAY - NANO INNOV, Bât. 861 - Point courrier 173, 91191 Gif Sur Yvette Cedex, France ' Lcoms, University of Lorraine, 7, rue Marconi, 57070, Metz, France ' Lire Laboratory, Constantine 2 - Abdelhamid Mehri University, BP 325, Route Ain El Bey, 25017, Constantine, Algeria ' Lcoms, University of Lorraine, 7, rue Marconi, 57070, Metz, France

Abstract: In this paper, we propose an automatic selection and natural feature tracking method that uses a monocular camera for path capturing and guides the user showing him the path to be followed. The application targets Alzheimer patients for helping them in their indoor moves. By offering an automatic selection of features, the user intervention and prior knowledge of the working environment would not be required to assure the good working of the system. The general principle of the proposed method is to record the path to be followed, and then recognise it in real time using purely visual methods, using only a single camera as an acquisition sensor. The devised system could be implemented on augmented-reality glasses with one single built-in camera. The experimental results have shown that the proposed method is very promising and the application could follow accurately the required path in real time, with a satisfying robustness in a fully-contrasted and static environment.

Keywords: natural features; matching; local descriptor; optical flow; Alzheimer disease; augmented reality glasses.

DOI: 10.1504/IJCVR.2018.091982

International Journal of Computational Vision and Robotics, 2018 Vol.8 No.2, pp.201 - 220

Received: 02 Jan 2016
Accepted: 17 Jul 2016
Published online: 24 May 2018 *

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