Collaborative algorithms that combine AI with IoT towards monitoring and control system

Zhang, T; Zhao, Y; Jia, W; Chen, MY

Collaborative algorithms that combine AI with IoT towards monitoring and control system

Zhang, T Zhao, Y Jia, W

Chen, MY

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2021, 125, pp. 677-686
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Zhang, T
dc.contributor.author	Zhao, Y
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	Chen, MY
dc.date.accessioned	2021-11-09T00:03:11Z
dc.date.available	2021-11-09T00:03:11Z
dc.date.issued	2021-12-01
dc.identifier.citation	Future Generation Computer Systems, 2021, 125, pp. 677-686
dc.identifier.issn	0167-739X
dc.identifier.uri	http://hdl.handle.net/10453/151415
dc.description.abstract	Recently, a new IoT structure known as the Artificial Intelligence of Things (AIoT) comes into play. Crowd counting is a promising field in data analysis of AIoT, however, due to poor transparency and high data security risks, developing a novel network architecture that can precisely elevate the counting of heavy crowd is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. The focus of this work is on the effective design of IoT framework and deep learning algorithm towards security of smart city. The system can be used to estimate the crowd traffic in public places, and can prevent the occurrence of congestion, stampede and other accidents, such as stations, airports, large-scale exhibitions, tourist attractions and other places. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically tracks densely crowd areas by controlling the video monitoring device. Moreover, the cloud platform can be controlled through the network. Our proposed algorithms are composed of two main aspects, i.e., division and focus. Firstly, we propose a novel density-adaptive Gaussian kernel to elevate the quality of density maps. Then, we propose a module based on conditional random fields for feature fusion. Finally, we propose a block segmentation module to predict our segmentation results and extract the context-aware information in segmentation stage. Experiments on our captured data, the Shanghai Tech, UCF_CC_50 and UCF_QNRF datasets demonstrate that our solution has obtained better performance and lower count errors over the state of the art.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Future Generation Computer Systems
dc.relation.isbasedon	10.1016/j.future.2021.07.008
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Distributed Computing
dc.title	Collaborative algorithms that combine AI with IoT towards monitoring and control system
dc.type	Journal Article
utslib.citation.volume	125
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-12-01T00:00:00+1000Z
dc.date.updated	2021-11-09T00:03:09Z
pubs.publication-status	Accepted
pubs.volume	125

Abstract:

Recently, a new IoT structure known as the Artificial Intelligence of Things (AIoT) comes into play. Crowd counting is a promising field in data analysis of AIoT, however, due to poor transparency and high data security risks, developing a novel network architecture that can precisely elevate the counting of heavy crowd is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. The focus of this work is on the effective design of IoT framework and deep learning algorithm towards security of smart city. The system can be used to estimate the crowd traffic in public places, and can prevent the occurrence of congestion, stampede and other accidents, such as stations, airports, large-scale exhibitions, tourist attractions and other places. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically tracks densely crowd areas by controlling the video monitoring device. Moreover, the cloud platform can be controlled through the network. Our proposed algorithms are composed of two main aspects, i.e., division and focus. Firstly, we propose a novel density-adaptive Gaussian kernel to elevate the quality of density maps. Then, we propose a module based on conditional random fields for feature fusion. Finally, we propose a block segmentation module to predict our segmentation results and extract the context-aware information in segmentation stage. Experiments on our captured data, the Shanghai Tech, UCF_CC_50 and UCF_QNRF datasets demonstrate that our solution has obtained better performance and lower count errors over the state of the art.

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http://hdl.handle.net/10453/151415