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Evaluation of embedded camera systems for autonomous wheelchairs
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2019 (English)In: VEHITS 2019 - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems, SciTePress , 2019, p. 76-85Conference paper, Published paper (Refereed)
Abstract [en]

Autonomously driving Power Wheelchairs (PWCs) are valuable tools to enhance the life quality of their users. In order to enable truly autonomous PWCs, camera systems are essential. Image processing enables the development of applications for both autonomous driving and obstacle avoidance. This paper explores the challenges that arise when selecting a suitable embedded camera system for these applications. Our analysis is based on a comparison of two well-known camera principles, Stereo-Cameras (STCs) and Time-of-Flight (ToF) cameras, using the standard deviation of the ground plane at various lighting conditions as a key quality measure. In addition, we also consider other metrics related to both the image processing task and the embedded system constraints. We believe that this assessment is valuable when choosing between using STC or ToF cameras for PWCs.

Place, publisher, year, edition, pages
SciTePress , 2019. p. 76-85
Keywords [en]
Autonomous Wheelchair, Embedded Camera System, RANSAC, Stereo Camera, Time-of-Flight, Cameras, Embedded systems, Intelligent systems, Intelligent vehicle highway systems, Quality control, Traffic control, Wheelchairs, Camera systems, Stereo cameras, Time of flight, Stereo image processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-36685ISI: 000570379100008Scopus ID: 2-s2.0-85067542836ISBN: 9789897583742 (print)OAI: oai:DiVA.org:miun-36685DiVA, id: diva2:1336506
Conference
5th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2019, Heraklion, Crete, Greece, 3 May 2019 through 5 May 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)Available from: 2019-07-09 Created: 2019-07-09 Last updated: 2021-11-23Bibliographically approved
In thesis
1. Semi-Autonomous Navigation of Powered Wheelchairs: 2D/3D Sensing and Positioning Methods
Open this publication in new window or tab >>Semi-Autonomous Navigation of Powered Wheelchairs: 2D/3D Sensing and Positioning Methods
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous driving and assistance systems have become a reality for the automotive industry to improve driving safety in the car. Hence, the cars use a variety of sensors, cameras and image processing techniques to measure their surroundings and control their direction, braking and speed for obstacle avoidance or autonomously driving applications.Like the automotive industry, powered wheelchairs also require safety systems to ensure their operation, especially when the user has controlling limitations, but also to develop new applications to improve its usability. One of the applications is focused on developing a new contactless control of a powered wheelchair using the position of a caregiver beside it as a control reference. Contactless control can prevent control errors, but it can also provide better and more equal communication between the wheelchair user and the caregiver

This thesis evaluates the camera requirements for a contactless powered wheelchair control and the 2D/3D image processing techniques for caregiver recognition and position measurement beside the powered wheelchair. The research evaluates the strength and limitations of different depth camera technologies for caregiver feet detection above the ground plane to select the proper camera for the application. Then, a hand-crafted 3D object descriptor is evaluated for caregiver feet recognition and compared with respect to a state-of-the-art deep learning object detector. Results for both methods are good, however, the hand-crafted descriptor suffers from segmentation errors and consequently, their accuracy is lower. After the depth camera and image processing techniques evaluation, results show that it is possible to use only an RGB camera to recognize and measure his or her relative position.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2021. p. 64
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X
Keywords
3D object recognition, YOLO, YOLO-Tiny, 3DHOG, Histogram-of-Oriented-Gradients, ModelNet40, Feature descriptor, Intel RealSense, Depth camera, Wheelchair
National Category
Computer graphics and computer vision
Identifiers
urn:nbn:se:miun:diva-43829 (URN)978-91-89341-32-6 (ISBN)
Public defence
2021-12-09, O102, Mittuniversitetet, Sundsvall, 16:06 (English)
Opponent
Supervisors
Note

Vid tidpunkten för disputationen var följande delarbeten opublicerade: delarbete 5 inskickat.

At the time of the doctoral defence the following papers were unpublished: paper 5 submitted.

Available from: 2021-11-24 Created: 2021-11-23 Last updated: 2025-02-07Bibliographically approved

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Vilar, CristianThörnberg, BennyKrug, Silvia

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