Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

Louis Lecrosnier, Redouane Khemmar, Nicolas Ragot, Benoit Decoux, Romain Rossi, Naceur Kefi and Jean-Yves Ertaud
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Louis Lecrosnier: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
Redouane Khemmar: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
Nicolas Ragot: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
Benoit Decoux: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
Romain Rossi: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
Naceur Kefi: SUP’COM: École Supérieure des Communications de Tunis, Carthage University, Aryanah 2080, Tunis
Jean-Yves Ertaud: École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France

IJERPH, 2020, vol. 18, issue 1, 1-17

Abstract: This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Keywords: object detection; tracking; distance estimation; smart mobility; object localization; distance measurement; deep learning; computer vision; semantic map (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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