Smart Robotic Wheelchair for Bus Boarding Using CNN Combined with Hough Transforms. Ali, S., Al Mamun, S., Fukuda, H., Lam, A., Kobayashi, Y., & Kuno, Y. In Huang, D., Gromiha, M. M., Han, K., & Hussain, A., editors, Intelligent Computing Methodologies, of Lecture Notes in Computer Science, pages 163–172, Cham, 2018. Springer International Publishing. doi abstract bibtex In recent times, several smart robotic wheelchair research studies have been conducted for the sake of providing a safe and comfortable ride for the user with real-time autonomous operations like object recognition. Further reliability support is essential for such wheelchairs to perform in real-time, common actions like boarding buses or trains. In this paper, we propose a smart wheelchair that can detect buses and precisely recognize bus doors and whether they are opened or closed for automated boarding. We use a modified simple CNN algorithm (i.e. modified Tiny-YOLO) as a base network on the CPU for fast detection of buses and bus doors. After that, we feed the detected information of our Hough line transform based method for accurate localization information of open bus doors. This information is indispensable for our bus-boarding robotic wheelchair to board buses. To evaluate the performance of our proposed method, we also compare the accuracy of our modified Tiny-YOLO and our proposed combined detection method with the original ground truth.
@inproceedings{ali_smart_2018,
address = {Cham},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Smart {Robotic} {Wheelchair} for {Bus} {Boarding} {Using} {CNN} {Combined} with {Hough} {Transforms}},
isbn = {978-3-319-95957-3},
doi = {10.1007/978-3-319-95957-3_18},
abstract = {In recent times, several smart robotic wheelchair research studies have been conducted for the sake of providing a safe and comfortable ride for the user with real-time autonomous operations like object recognition. Further reliability support is essential for such wheelchairs to perform in real-time, common actions like boarding buses or trains. In this paper, we propose a smart wheelchair that can detect buses and precisely recognize bus doors and whether they are opened or closed for automated boarding. We use a modified simple CNN algorithm (i.e. modified Tiny-YOLO) as a base network on the CPU for fast detection of buses and bus doors. After that, we feed the detected information of our Hough line transform based method for accurate localization information of open bus doors. This information is indispensable for our bus-boarding robotic wheelchair to board buses. To evaluate the performance of our proposed method, we also compare the accuracy of our modified Tiny-YOLO and our proposed combined detection method with the original ground truth.},
language = {en},
booktitle = {Intelligent {Computing} {Methodologies}},
publisher = {Springer International Publishing},
author = {Ali, Sarwar and Al Mamun, Shamim and Fukuda, Hisato and Lam, Antony and Kobayashi, Yoshinori and Kuno, Yoshonori},
editor = {Huang, De-Shuang and Gromiha, M. Michael and Han, Kyungsook and Hussain, Abir},
year = {2018},
keywords = {Bus door detection, CNN, Hough transformation},
pages = {163--172},
}
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In this paper, we propose a smart wheelchair that can detect buses and precisely recognize bus doors and whether they are opened or closed for automated boarding. We use a modified simple CNN algorithm (i.e. modified Tiny-YOLO) as a base network on the CPU for fast detection of buses and bus doors. After that, we feed the detected information of our Hough line transform based method for accurate localization information of open bus doors. This information is indispensable for our bus-boarding robotic wheelchair to board buses. 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Further reliability support is essential for such wheelchairs to perform in real-time, common actions like boarding buses or trains. In this paper, we propose a smart wheelchair that can detect buses and precisely recognize bus doors and whether they are opened or closed for automated boarding. We use a modified simple CNN algorithm (i.e. modified Tiny-YOLO) as a base network on the CPU for fast detection of buses and bus doors. After that, we feed the detected information of our Hough line transform based method for accurate localization information of open bus doors. This information is indispensable for our bus-boarding robotic wheelchair to board buses. 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