Journal Home > Volume 3
CAAI Artificial Intelligence Research 2024, 3: 9150028 https://doi.org/10.26599/AIR.2024.9150028
Article | Open Access | Issue | Published: 22 April 2024

Feature-Centric Video Transmission and Analytics in Large-Scale Internet of Video Things

Show Author's Information Hongan Wei1,  Yuxiang Liu1,  Kejian Hu1,  Liqun Lin1,2( ),  Youjia Chen1,  Tiesong Zhao1,  Wanjian Feng3
Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
Yealink Inc., Xiamen 361009, China
Keywords:
resource allocation, edge computing, video transmission, Internet of Video Things (IoVT)
Cite this article:
Wei H, Liu Y, Hu K, et al. Feature-Centric Video Transmission and Analytics in Large-Scale Internet of Video Things. CAAI Artificial Intelligence Research, 2024, 3: 9150028. https://doi.org/10.26599/AIR.2024.9150028
Download citation
EndNote(RIS)
BibTeX

158

Views

22

Downloads

Citations

0

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text About this article

The interconnection of large-scale visual sensors is called the Internet of Video Things (IoVT), which brings a qualitative leap to the interaction of urban information. However, communication delay and resource allocation have brought challenges to the development of IoVT. In this paper, we propose a novel city surveillance IoVT architecture to improve performance. This paradigm consists of front-end target region capture, edge computing and cloud-end feature matching, which can adapt the channel and computing resource allocation ratio flexibly, avoiding communication link congestion caused by unnecessary video uploading. Simulation results show that the proposed scheme is feasible, and can realize efficient data transmission and analysis in an IoVT-based smart city.


menu
Abstract
Full text
Outline
About this article

Feature-Centric Video Transmission and Analytics in Large-Scale Internet of Video Things

Show Author's information Hongan Wei1, Yuxiang Liu1, Kejian Hu1, Liqun Lin1,2( ), Youjia Chen1, Tiesong Zhao1, Wanjian Feng3
Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
Yealink Inc., Xiamen 361009, China

Abstract

The interconnection of large-scale visual sensors is called the Internet of Video Things (IoVT), which brings a qualitative leap to the interaction of urban information. However, communication delay and resource allocation have brought challenges to the development of IoVT. In this paper, we propose a novel city surveillance IoVT architecture to improve performance. This paradigm consists of front-end target region capture, edge computing and cloud-end feature matching, which can adapt the channel and computing resource allocation ratio flexibly, avoiding communication link congestion caused by unnecessary video uploading. Simulation results show that the proposed scheme is feasible, and can realize efficient data transmission and analysis in an IoVT-based smart city.

Keywords: resource allocation, edge computing, video transmission, Internet of Video Things (IoVT)

References(20)

[1]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, Internet of Things: A survey on enabling technologies, protocols, and applications, IEEE Commun. Surv. Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
DOI Google Scholar
[2]

Z. Zhao, Y. Lai, Y. Wang, W. Jia, and H. He, A few-shot learning based approach to IoT traffic classification, IEEE Commun. Lett., vol. 26, no. 3, pp. 537–541, 2022.
DOI Google Scholar
[3]

L. Duan, Y. Lou, S. Wang, W. Gao, and Y. Rui, AI-oriented large-scale video management for smart city: Technologies, standards, and beyond, IEEE MultiMedia, vol. 26, no. 2, pp. 8–20, 2019.
DOI Google Scholar
[4]

Y. Lou, L. Y. Duan, Y. Luo, Z. Chen, T. Liu, S. Wang, and W. Gao, Towards efficient front-end visual sensing for digital retina: A model-centric paradigm, IEEE Trans. Multimedia, vol. 22, no. 11, pp. 3002–3013, 2020.
DOI Google Scholar
[5]

C. W. Chen, Internet of video things: Next-generation IoT with visual sensors, IEEE Internet Things J., vol. 7, no. 8, pp. 6676–6685, 2020.
DOI Google Scholar
[6]

Y. Chen, T. Zhao, P. Cheng, M. Ding, and C. W. Chen, Joint front-edge-cloud IoVT analytics: Resource-effective design and scheduling, IEEE Internet Things J., vol. 9, no. 23, pp. 23941–23953, 2022.
DOI Google Scholar
[7]
O. Kochan, M. Beshley, H. Beshley, Y. Shkoropad, I. Ivanochko, and N. Seliuchenko, SDN-based Internet of video things platform enabling real-time edge/cloud video analytics, in Proc. 17th Int. Conf. Experience of Designing and Application of CAD Systems (CADSM), Jaroslaw, Poland, 2023, pp. 1–5.
DOI
[8]

X. Liu, Research on intelligent visual image feature region acquisition algorithm in Internet of Things framework, Comput. Commun., vol. 151, pp. 299–305, 2020.
DOI Google Scholar
[9]

R. Kilani, A. Zouinkhi, E. Bajic, and M. N. Abdelkrim, Socialization of smart communicative objects in industrial Internet of Things, IFAC-PapersOnLine, vol. 55, no. 10, pp. 1924–1929, 2022.
DOI Google Scholar
[10]

C. Zhuansun, K. Yan, G. Zhang, Z. Xiong, and C. Huang, Hypergraph-based resource allocation for ultra-dense wireless network in industrial IoT, IEEE Commun. Lett., vol. 26, no. 9, pp. 2106–2110, 2022.
DOI Google Scholar
[11]

W. Ji, L. Duan, X. Huang, and Y. Chai, Astute video transmission for geographically dispersed devices in visual IoT systems, IEEE Trans. Mob. Comput., vol. 21, no. 2, pp. 448–464, 2022.
DOI Google Scholar
[12]

W. J. Thompson, Poisson distributions, Comput. Sci. Eng., vol. 3, no. 3, pp. 78–82, 2001.
DOI Google Scholar
[13]
T. H. Wu, T. W. Wang, and Y. Q. Liu, Real-time vehicle and distance detection based on improved yolo v5 network, in Proc. 3rd World Symp. on Artificial Intelligence (WSAI), Guangzhou, China, 2021, pp. 24–28.
[14]

C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, 1948.
DOI Google Scholar
[15]

Q. Ye, W. Zhuang, X. Li, and J. Rao, End-to-end delay modeling for embedded VNF chains in 5G core networks, IEEE Internet Things J., vol. 6, no. 1, pp. 692–704, 2019.
DOI Google Scholar
[16]
P. Dendorfer, H. Rezatofighi, A. Milan, J. Shi, D. Cremers, I. Reid, S. Roth, K. Schindler, and L. Leal-Taixé, MOT20: A benchmark for multi object tracking in crowded scenes, arXiv preprint arXiv: 2003.09003, 2020.
[17]

Z. Li, S. Lu, L. Lan, and Q. Liu, Crowd counting in complex scenes based on an attention aware CNN network, J. Vis. Commun. Image Represent., vol. 87, p. 103591, 2022.
DOI Google Scholar
[18]
J. Jin, J. Ma, L. Liu, L. Lu, G. Wu, D. Huang, and N. Qin, Design of UAV video and control signal real-time transmission system based on 5G network, in Proc. IEEE 16th Conf. Industrial Electronics and Applications (ICIEA), Chengdu, China, 2021, pp. 533–537.
DOI
[19]

J. Ren, G. Yu, Y. He, and G. Y. Li, Collaborative cloud and edge computing for latency minimization, IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 5031–5044, 2019.
DOI Google Scholar
[20]

W. Gao, Y. Tian, and J. Wang, Digital retina: Revolutionizing camera systems for the smart city (in Chinese), Sci. Sin. Informationis, vol. 48, no. 8, pp. 1076–1082, 2018.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 10 August 2023
Revised: 23 November 2023
Accepted: 19 January 2024
Published: 22 April 2024
Issue date: December 2024

Copyright

© The author(s) 2024.

Acknowledgements

Acknowledgment

This work was supported by the Natural Science Foundations of Fujian Province, China (No. 2023J01395).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return