Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility
Bibliographic Data
| ID | 15464508 |
|---|---|
| Authors | Louis Lecrosnier (0000-0001-8570-3781, École Supérieure d'Ingénieurs en Génie Électrique, corresponding author), Redouane Khemmar (0000-0002-6230-2966, École Supérieure d'Ingénieurs en Génie Électrique), Nicolas Ragot (0000-0001-5360-2185, École Supérieure d'Ingénieurs en Génie Électrique), Benoît Decoux (0000-0003-4037-2880, École Supérieure d'Ingénieurs en Génie Électrique), Romain Rossi (0000-0002-5130-4798, École Supérieure d'Ingénieurs en Génie Électrique), Naceur Kefi (University of Carthage), Jean-Yves Ertaud (University of Carthage) |
| Year | 2020 |
| Volume | 18 |
| Issue | 1 |
| Pages | 91-91 |
| Publication date | 2020-12-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18010091 |
| PMID | 33374389 |
| OpenAlex | W3116258144 |
| Language | EN |
| Citations received | 1 |
| References cited | 10 |
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
Computer vision · Deep learning · Health care · Human–computer interaction · Object detection · Pattern recognition (psychology · Physical medicine and rehabilitation · Tracking (education · Wheelchair · World Wide Web · Computer Science · Gaze Tracking and Assistive Technology · Hand Gesture Recognition Systems · Medicine · Psychology · Video Surveillance and Tracking Methods · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |