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Urban footpath image dataset to assess pedestrian mobility

Gurram Munirathnam, Venkatesh orcid logoORCID: 0000-0002-4393-9267, Pereira, Bianca and Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 (2021) Urban footpath image dataset to assess pedestrian mobility. In: UrbanMM-21, 20–24 Oct 2021, Chengdu, China (Online). ISBN 978-1-4503-8669-2/21/10

Abstract
This paper presents an urban footpath image dataset captured through crowdsourcing using the mapillary service (mobile ap- plication) and demonstrating its use for data analytics applications by employing object detection and image segmentation. The study was motivated by the unique, individual mobility challenges that many people face in navigating public footpaths, in particular those who use mobility aids such as long cane, guide digs, crutches, wheelchairs, etc., when faced with changes in pavement surface (tactile pavements) or obstacles such as bollards and other street furniture. Existing image datasets are generally captured from an instrumented vehicle and do not provide sufficient or adequate images of the footpaths from the pedestrian perspective. A citizen science project (Crowd4Access) worked with user groups and volunteers to gather a sample image dataset resulting in a set of 39,642 images collected in a range of different conditions. Preliminary studies to detect tactile pavements and perform semantic segmentation using state-of-the-art computer vision models demonstrate the utility of this dataset to enable better understanding of urban mobility issues.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:urban elements; convolution neural network; object detection; semantic segmentation; street-view analytics
Subjects:Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the 1st International Workshop on Multimedia Computing for Urban Data (UrbanMM ’21). . Association for Computing Machinery (ACM). ISBN 978-1-4503-8669-2/21/10
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3475721.3484313
Copyright Information:© 2021 The Owner/ Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) under Grant Numbers SFI/12/RC/2289_P2, 16/SP/3804, European Regional Development Fund
ID Code:26261
Deposited On:26 Oct 2021 15:42 by Venkatesh Gurram Munirathnam . Last Modified 16 Jan 2023 16:08
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