Next Article in Journal
Monocular 3D Body Shape Reconstruction under Clothing
Previous Article in Journal
Bay Leaf Extract-Based Near-Infrared Fluorescent Probe for Tissue and Cellular Imaging
Article

Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation

1
Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden
2
System Design Department, IMMS Institut für Mikroelektronik- und Mechatronik-Systeme Gemeinnützige GmbH (IMMS GmbH), Ehrenbergstraße 27, 98693 Ilmenau, Germany
3
Faculty of Science, University of Ontario Institute of Technology, 2000 Simcoe St. N., Oshawa, ON L1G OC5, Canada
*
Authors to whom correspondence should be addressed.
Academic Editor: Ioannis Pratikakis
J. Imaging 2021, 7(12), 255; https://doi.org/10.3390/jimaging7120255
Received: 28 September 2021 / Revised: 18 November 2021 / Accepted: 19 November 2021 / Published: 30 November 2021
(This article belongs to the Section Computer Vision and Pattern Recognition)
Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions. View Full-Text
Keywords: 3D object recognition; YOLO; YOLO-Tiny; 3DHOG; histogram of oriented gradients; ModelNet40; feature descriptor; Intel RealSense; depth camera; wheelchair 3D object recognition; YOLO; YOLO-Tiny; 3DHOG; histogram of oriented gradients; ModelNet40; feature descriptor; Intel RealSense; depth camera; wheelchair
Show Figures

Figure 1

MDPI and ACS Style

Giménez, C.V.; Krug, S.; Qureshi, F.Z.; O’Nils, M. Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation. J. Imaging 2021, 7, 255. https://doi.org/10.3390/jimaging7120255

AMA Style

Giménez CV, Krug S, Qureshi FZ, O’Nils M. Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation. Journal of Imaging. 2021; 7(12):255. https://doi.org/10.3390/jimaging7120255

Chicago/Turabian Style

Giménez, Cristian V., Silvia Krug, Faisal Z. Qureshi, and Mattias O’Nils. 2021. "Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation" Journal of Imaging 7, no. 12: 255. https://doi.org/10.3390/jimaging7120255

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop