Indoor Object C1assification for Autonomous Navigation Assistance Based on Deep CNN Model

Mouna Aftf,Yahia Said,Riadh Ayachi,Edwige Pissaloux,Mohamed Atri

doi:10.1109/iwmn.2019.8805042

Indoor Object C1assification for Autonomous Navigation Assistance Based on Deep CNN Model

Mouna Aftf, Yahia Said + Show 3 more

https://doi.org/10.1109/iwmn.2019.8805042

Copy DOI

Publication Date: Jul 1, 2019

Citations: 19

Affiliation: University of Monastir, University of Rouen, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, French National Centre for Scientific Research

#Indoor Object #Deep Convolutional Neural Network + Show 8 more

Abstract
Full-Text PDF
Similar Papers

Abstract

Indoor object classification is a key element for indoor navigation assistance systems. Indoor objects knowledge helps Visually Impaired People (VIP) in their indoor navigation and facilitates their daily life. This paper proposes a new classification system used especially for indoor object recognition based on Deep Convolutional Neural Network (DCNN) model which can be implemented on mobile embedded platforms. Experimental results obtained using natural images (with natural illumination) from the MCIndoor 20000 dataset show that the proposed approach achieves almost100% accuracy for indoor object classification.

Full Text