Building a Crowdsourcing based Disabled Pedestrian Level of Service routing applicationusing Computer Vision and Machine Learning
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | the 16th Conference of IEEE Consumer Communications & Networking Conference (CCNC) |
| Year: | 2019 |
| Month: | January |
| Pages: | 1-5 |
| Publisher: | IEEE |
| Location: | Las Vegas, USA |
| ISSN: | 2331-9860 |
| ISBN: | 978-1-5386-5553-5 |
| URL: | https://ieeexplore.ieee.org/do... |
| DOI: | 10.1109/CCNC.2019.8651850 |
| Abstract: | The availability of global and scalable tools to assess disabled pedestrian level of service (DPLoS) is a real need, yet still a challenge in today’s world. This is due to the lack of tools that can ease the measurement of a level of service adapted to disabled people, and also to the limitation concerns about the availability of information regarding the existing level of service, especially in real time. This paper describes preliminary results to progress on those needs. It also includes a design for a navigation tool that can help a disabled person move around a city by suggesting the most adapted routes according to the person’s disabilities. The main topics are how to use advanced computer vision technologies, and how to benefit from the prevalence of handheld devices. Our approach intends to show how crowdsourcing techniques can improve data quality by gathering and combining up-to-date data with valuable field observations. |
| Keywords: | computer vision, crowdsourcing, Disabled Pedestrian Level, machine learning, Routing |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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