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

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 object...

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Veröffentlicht in:International journal of environmental research and public health Jg. 18; H. 1; S. 91
Hauptverfasser: Lecrosnier, Louis, Khemmar, Redouane, Ragot, Nicolas, Decoux, Benoit, Rossi, Romain, Kefi, Naceur, Ertaud, Jean-Yves
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 24.12.2020
Schlagworte:
Algorithms
Deep Learning
Disabled Persons
Engineering Sciences
Environment Design
Humans
Signal and Image processing
Wheelchairs
deep learning
smart mobility
distance measurement
object localization
computer vision
distance estimation
object detection
semantic map
tracking
ISSN:1660-4601, 1661-7827, 1660-4601
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
PMCID: PMC7796357
These authors contributed equally to this work.
Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
SUP’COM: École Supérieure des Communications de Tunis, Carthage University, El Ghazala Ariana 2083, Tunisia.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph18010091