Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2
Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchai...
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| Published in: | Future generation computer systems Vol. 92; pp. 157 - 161 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2019
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchairs, and the dangers of driving have been noted. Therefore, there is a need to improve accident factors, reduce accidents and improve the convenience of electric wheelchairs by using automation. Environmental recognition is the key technology for developing autonomous electric wheelchairs. Environmental recognition includes self-position estimation, the recognition of sidewalks, crosswalks and traffic lights, and moving object predictions. To solve these problems, this paper develops a system for detecting sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2, which is an object detection algorithm that applies convolutional neural networks (CNNs). We detect the object through YOLOv2 and perform processing steps, such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the average AUC of the detection rate is 0.587.
•We develop a system for detecting sidewalks, crosswalks and traffic lights.•We develop the object recognition methods using a modified YOLOv2.•We detect the object use bounding box deletion and interpolation. |
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| AbstractList | Currently, the aging population is growing in Japan, and the needs for the utilization of welfare equipment are consequently increasing. The electric wheelchair, which is a convenient transportation tool, has rapidly become popular. However, many accidents have occurred when using electric wheelchairs, and the dangers of driving have been noted. Therefore, there is a need to improve accident factors, reduce accidents and improve the convenience of electric wheelchairs by using automation. Environmental recognition is the key technology for developing autonomous electric wheelchairs. Environmental recognition includes self-position estimation, the recognition of sidewalks, crosswalks and traffic lights, and moving object predictions. To solve these problems, this paper develops a system for detecting sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2, which is an object detection algorithm that applies convolutional neural networks (CNNs). We detect the object through YOLOv2 and perform processing steps, such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the average AUC of the detection rate is 0.587.
•We develop a system for detecting sidewalks, crosswalks and traffic lights.•We develop the object recognition methods using a modified YOLOv2.•We detect the object use bounding box deletion and interpolation. |
| Author | Tan, Joo-Kooi Kim, Hyoungseop Sakai, Yuki Lu, Huimin |
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| Cites_doi | 10.1109/ICMLA.2016.0144 10.1007/978-3-319-46448-0_2 10.1016/j.patcog.2017.11.007 10.1109/CVPR.2014.81 10.1109/TIP.2015.2427518 10.1007/978-3-662-45286-8_43 10.1109/CVPR.2017.690 |
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| References_xml | – reference: (2018/2/2access) (in Japanese). – reference: (2018/1/9 access) (in Japanese). – reference: Ministry of Internal Affairs and Communications, Toukei Kara Mita Wagakunino Koreisha (The elderly in our country viewed from statistics), – volume: 76 start-page: 323 year: 2018 end-page: 338 ident: b3 article-title: Deep visual tracking: Review and experimental comparison publication-title: Pattern Recognit. – reference: Y. Alkhorsid, et al. Road detection through supervised classification, in: IEEE International Conference on Machine Learning and Applications, 2016, pp. 806–809. – reference: W. Liu, et al. SSD: single shot multibox detector, in: European Conference on Computer Vision, 2015, pp. 21–37. – reference: Bharat Singh, et al. R-FCN-3000 at 30fps: Decoupling detection and classification, arXiv preprint – reference: Electric wheelchair safety dissemination association, Shukka Disu No Suii (Trends in shipments), – reference: J. Redmon, et al. YOLO9000: Better, Faster, Stronger, in: IEEE Conference on Computer Vision and Patern Recognition, 2017, pp. 7263–7271. – volume: 24 start-page: 2646 year: 2015 end-page: 2657 ident: b4 article-title: Inverse sparse tracker with a locally weighted distance metric publication-title: IEEE Trans. Image Process. – reference: , 2016. – reference: F. Ran, et al. Vision-based lane detection algorithm in urban traffic scenes, in: International Conference on Life System Modeling and Simulation and International Conference on Intelligent Computing for Sustainable Energy and Environment, 2014, pp. 409-419. – reference: R. Girshick, et al. Rich feature hierarchies for accurate object detection and semantic segmentation, in: IEEE Conference on Computer Vision and Patern Recognition, 2014, pp. 580–587. – ident: 10.1016/j.future.2018.09.068_b5 doi: 10.1109/ICMLA.2016.0144 – ident: 10.1016/j.future.2018.09.068_b10 – ident: 10.1016/j.future.2018.09.068_b2 – ident: 10.1016/j.future.2018.09.068_b9 doi: 10.1007/978-3-319-46448-0_2 – ident: 10.1016/j.future.2018.09.068_b1 – volume: 76 start-page: 323 year: 2018 ident: 10.1016/j.future.2018.09.068_b3 article-title: Deep visual tracking: Review and experimental comparison publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.11.007 – ident: 10.1016/j.future.2018.09.068_b8 doi: 10.1109/CVPR.2014.81 – volume: 24 start-page: 2646 issue: 9 year: 2015 ident: 10.1016/j.future.2018.09.068_b4 article-title: Inverse sparse tracker with a locally weighted distance metric publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2427518 – ident: 10.1016/j.future.2018.09.068_b6 doi: 10.1007/978-3-662-45286-8_43 – ident: 10.1016/j.future.2018.09.068_b7 doi: 10.1109/CVPR.2017.690 |
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| Title | Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2 |
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