Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation
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 o...
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| Formáid: | Alt |
| Teanga: | English |
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MDPI AG
2021-11-01
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| Sraith: | Journal of Imaging |
| Ábhair: | |
| Rochtain ar líne: | https://www.mdpi.com/2313-433X/7/12/255 |
| _version_ | 1827671844947230720 |
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| author | Cristian Vilar Giménez Silvia Krug Faisal Z. Qureshi Mattias O’Nils |
| author_facet | Cristian Vilar Giménez Silvia Krug Faisal Z. Qureshi Mattias O’Nils |
| author_sort | Cristian Vilar Giménez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:49:30Z |
| format | Article |
| id | doaj.art-22d59d3ac56b4f968950c7b901b56962 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T03:49:30Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-22d59d3ac56b4f968950c7b901b569622023-11-23T09:00:35ZengMDPI AGJournal of Imaging2313-433X2021-11-0171225510.3390/jimaging7120255Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair NavigationCristian Vilar Giménez0Silvia Krug1Faisal Z. Qureshi2Mattias O’Nils3Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, SwedenDepartment of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, SwedenDepartment of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, SwedenDepartment of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, SwedenPowered 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.https://www.mdpi.com/2313-433X/7/12/2553D object recognitionYOLOYOLO-Tiny3DHOGhistogram of oriented gradientsModelNet40 |
| spellingShingle | Cristian Vilar Giménez Silvia Krug Faisal Z. Qureshi Mattias O’Nils Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation Journal of Imaging 3D object recognition YOLO YOLO-Tiny 3DHOG histogram of oriented gradients ModelNet40 |
| title | Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation |
| title_full | Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation |
| title_fullStr | Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation |
| title_full_unstemmed | Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation |
| title_short | Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation |
| title_sort | evaluation of 2d 3d feet detection methods for semi autonomous powered wheelchair navigation |
| topic | 3D object recognition YOLO YOLO-Tiny 3DHOG histogram of oriented gradients ModelNet40 |
| url | https://www.mdpi.com/2313-433X/7/12/255 |
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