TY  - JOUR
PY  - 2021//
TI  - Deep learning-based object detection, localisation and tracking for smart wheelchair healthcare mobility
JO  - International journal of environmental research and public health
A1  - Lecrosnier, Louis
A1  - Khemmar, Redouane
A1  - Ragot, Nicolas
A1  - Decoux, Benoit
A1  - Rossi, Romain
A1  - Kefi, Naceur
A1  - Ertaud, Jean-Yves
SP  - e91
EP  - e91
VL  - 18
IS  - 1
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1661-7827
UR  - http://dx.doi.org/10.3390/ijerph18010091
ID  - ref1
ER  -