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

Author

Listed:
  • Louis Lecrosnier

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

  • Redouane Khemmar

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

  • Nicolas Ragot

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

  • Benoit Decoux

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

  • Romain Rossi

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

  • Naceur Kefi

    (SUP’COM: École Supérieure des Communications de Tunis, Carthage University, Aryanah 2080, Tunis
    These authors contributed equally to this work.
    SUP’COM: École Supérieure des Communications de Tunis, Carthage University, El Ghazala Ariana 2083, Tunisia.)

  • Jean-Yves Ertaud

    (École Supérieure d’Ingénieurs en Génie Électrique, 76800 Saint-Étienne-du-Rouvay, France
    SUP’COM: École Supérieure des Communications de Tunis, Carthage University, Aryanah 2080, Tunis
    Current Address: UNIROUEN, ESIGELEC, IRSEEM, Normandie University, 76000 Rouen, France.
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Louis Lecrosnier & Redouane Khemmar & Nicolas Ragot & Benoit Decoux & Romain Rossi & Naceur Kefi & Jean-Yves Ertaud, 2020. "Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:91-:d:467951
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