Towards digital retina in smart cities: A model generation, utilization and communication paradigm

Lou, Y; Duan, LY; Luo, Y; Chen, Z; Liu, T; Wang, S; Gao, W

Towards digital retina in smart cities: A model generation, utilization and communication paradigm

Lou, Y Duan, LY Luo, Y Chen, Z Liu, T Wang, S Gao, W

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2019, 2019-July, pp. 19-24
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Lou, Y
dc.contributor.author	Duan, LY
dc.contributor.author	Luo, Y
dc.contributor.author	Chen, Z
dc.contributor.author	Liu, T
dc.contributor.author	Wang, S
dc.contributor.author	Gao, W
dc.date	2019-07-08
dc.date.accessioned	2020-04-30T22:36:41Z
dc.date.available	2020-04-30T22:36:41Z
dc.date.issued	2019-07-01
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2019, 2019-July, pp. 19-24
dc.identifier.isbn	9781538695524
dc.identifier.issn	1945-7871
dc.identifier.issn	1945-788X
dc.identifier.uri	http://hdl.handle.net/10453/140373
dc.description.abstract	© 2019 IEEE. The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and communication paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed paradigm is demonstrated through a set of experiments.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo
dc.relation.ispartof	IEEE International Conference on Multimedia and Expo
dc.relation.isbasedon	10.1109/ICME.2019.00012
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Towards digital retina in smart cities: A model generation, utilization and communication paradigm
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
utslib.location.activity	Shanghai, China
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-04-30T22:36:23Z
pubs.finish-date	2019-07-12
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-08
pubs.volume	2019-July
utslib.start-page	19
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and communication paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed paradigm is demonstrated through a set of experiments.

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http://hdl.handle.net/10453/140373