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Urban access: query driven urban analytics platform for detecting complex accessibility event patterns using tactile surfaces

Salwala, Dhaval orcid logoORCID: 0000-0001-9787-4844, Yadav, Piyush orcid logoORCID: 0000-0002-4872-0205, Munirathnam, Venkatesh G. orcid logoORCID: 0000-0002-4393-9267, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Curry, Edward orcid logoORCID: 0000-0001-8236-6433 (2021) Urban access: query driven urban analytics platform for detecting complex accessibility event patterns using tactile surfaces. In: 1st International Workshop on Multimedia Computing for Urban Data (UrbanMM'21), 20–24 Oct 2021, Online (China). ISBN 978-1-4503-8669-2

Abstract
The smart city concept has now become one of the key enablers in urban city management. The adoption and permeation of ICT and AI-driven techniques have enabled the authorities to resolve poor urban planning issues with improved delivery of citizen services. Major urban problem is addressing the accessibility issue across cities road crossing and facilitating visually impaired people via well-defined infrastructure. The research presented in this paper emphasized urban analytics that studies the road crossings and challenges one faces when accessing the footpaths of a city using the Tactile surfaces. This work demonstrates a distributed event analytics platform- GNOSIS to detect complex accessibility event patterns. GNOSIS ingest video data streams from cities infrastructure such as CCTV and detect tactile surface event patterns using an ensemble of deep learning models using a declarative query language. The work analyzes mainly three types of tactile surfaceBlister, Cycleway and Directional, collected from different cities in Ireland using crowd-sourcing techniques. GNOSIS makes decisions in real-time based on the type of tactile surface, colour and the making pattern.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:urban analytics; accessibility complex event processing; deep neural networks; tactile surfaces
Subjects:Computer Science > Image processing
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: UrbanMM'21: Proceedings of the 1st International Workshop on Multimedia Computing for Urban Data. . Association for Computing Machinery (ACM). ISBN 978-1-4503-8669-2
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3475721.3484312
Copyright Information:© 2021 The Authors. Open Access
Funders:Science Foundation Ireland (SFI) grant SFI/12/RC/2289
ID Code:27384
Deposited On:25 Jul 2022 12:46 by Thomas Murtagh . Last Modified 26 Oct 2022 14:27
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