Skip to main content
Log in

RETRACTED ARTICLE: A City Monitoring System Based on Real-Time Communication Interaction Module and Intelligent Visual Information Collection System

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

This article was retracted on 11 April 2024

This article has been updated

Abstract

With the rapid development of society, the improvement of material level and the current situation of the large-scale population flow in China, the awareness of security is becoming more and more important in people’s life. With the rapid development of image processing and computer vision technology, people try to analyze, process and understand the collected video image automatically without human intervention. The intelligent video monitoring system collects video signals of interested objects in a dynamic scene through a camera, and processes and analyzes image information by a computer. Only by establishing a reasonable and effective urban video monitoring management system can government departments find out problems in the first time. The traditional highway monitoring and commanding traffic scheduling system based on GIS, which can obtain road traffic information and conduct traffic scheduling by remote sensing, has the disadvantage of poor effect on traffic scheduling. In this paper, real-time communication technology and computer vision acquisition technology are used to build a city monitoring system. The experimental results show that this method has strong timeliness and good monitoring effect. Compared with the state-of-the-art methodologies, the proposed framework is efficient and accurate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Change history

References

  1. Ramanan D, Forsyth D, Zisserman A (2007) Tracking people by learning their appearance. IEEE Trans Pattern Anal Mach Intell 29(1):65–81

    Article  Google Scholar 

  2. Hu W, Tan T, Wang L, Steve M (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern C Appl Rev 34(3):334–352

    Article  Google Scholar 

  3. Cucchiara R, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25(10):1337–1342

    Article  Google Scholar 

  4. Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77

    Article  Google Scholar 

  5. Zhao M, Zhao J (2006) A novel method for moving object detection in intelligent video surveillance systems. In: International conference on computational intelligence and security (CIS’2006), vol 2. GuangDong, Guangzhou, pp 1797–1800

  6. Han B, Comaniciu D, Davis L (2004) Sequential kernel density approximation through mode propagation: applications to background modeling. In: Proceedings of the sixth Asian conference on computer vision (ACCV’2004). Jeju, Korea, pp. 102–107

  7. Zhao CT, Wang HY, Li JW, Ying ZL (2013) A realization of face detection system based on ARM Linux. Appl Mech Mater 333:864–867

    Article  Google Scholar 

  8. Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  9. Culjak I, Abram D, Pribanic T, Dzapo H, Cifrek M (2012) A brief introduction to OpenCV[C]. MIPRO

  10. Rubini A (2002) Linux device driver, 2nd edn (trans: Yongming W). Power Press, Beijing

  11. Luo L, Hu YM (2015) Embedded inspection system for FPC based on machine vision. Comput Meas Control 19(2):303–305

    MathSciNet  Google Scholar 

  12. Shao H, Qiu YF (2014) The design of embedded positioning punching system based on machine vision. Opt Instrum 35(6):36–42

    Google Scholar 

  13. Shoham Y (1993) Agent-oriented programming. Artif Intell 60(1):51–92

    Article  MathSciNet  Google Scholar 

  14. Boming Z, Chuanlin Z, Wenehua W (2009) A multi-agent based distributed computing platform for new generationof EMS. In: Power systems conference and exposition, PSCE’09, IEEE/PES 2009, pp 7–9

  15. Wooldrideg M, Jennings NR (1995) Agents theories, architectures and language: a survey. Wooldridge and Jennings, Intelligent agents. Springer, Berlin

  16. Dou L (2005) Research on cooperation and coordination of multi-agent systems. Tianjin University, Tianjin

    Google Scholar 

  17. Keith J (2007) Video demystified: a hand book for the digital engineer, 5th edn. Newnes

  18. Rao KR, Kim DN, Hwang JJ (2013) Video coding standards: AVSChina, H.264/MPEG-4PART10, HEVC, VP6, DIRAC and VC-1. Springer, Berlin

    Google Scholar 

  19. Lu J (2013) MPEG-4/H.264 video codec engineering practice. Electronic Industry Press

  20. Al-Qazweeni J, Parol J, Kamal HA, Al-Enezi A, Bin-Nakhi A, Sun H, Büyüköztürk O (2020) Structural health monitoring system for Al-Hamra tower in Kuwait City. In: Gulf conference on sustainable built environment. Springer, Cham, pp 279–286

  21. Deng L, Li D (2019) Multimedia data stream information mining algorithm based on jointed neural network and soft clustering. Multimed Tools Appl 78(4):4021–4044

    Article  Google Scholar 

  22. Dong H, Zheng L, Pengjun Yu, Jiang Q, Yan W, Huang C, Yin B (2020) Characterization and application of lignin-carbohydrate complexes from lignocellulosic materials as antioxidant for scavenging in vitro and in vivo reactive oxygen species. ACS Sustain Chem Eng 8:256–266

    Article  Google Scholar 

  23. Yingying S, Lianjuan H, Jianan W, Huimin W (2019) Quantum-behaved RS-PSO-LSSVM method for quality prediction in parts production processes. Concurr Comput Pract Exp 9:e5522

    Google Scholar 

  24. Li D, Deng L, Gupta BB, Wang H, Choi C (2019) A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci 479:432–447

    Article  Google Scholar 

  25. Pavlichin DS, Jiao J, Weissman T (2019) Approximate profile maximum likelihood. J Mach Learn Res 20(122):1–55

    MathSciNet  Google Scholar 

  26. Yang T, Jiang YL, Gao S (2019) Design of underground integrated corridor monitoring system in Yude Road, Liupanshui City. In: IOP conference series: earth and environmental science, vol 218, no 1. IOP Publishing, pp 012108

  27. Li D, Deng L, Lee M, Wang H (2019) IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. Int J Inf Manag 49:533–545

    Article  Google Scholar 

  28. Jiao J, Han Y, Fischer-Hwang I, Weissman T (2019) Estimating the fundamental limits is easier than achieving the fundamental limits. IEEE Trans Inf Theory 65(10):6704–6715

    Article  MathSciNet  Google Scholar 

  29. Deng L, Li D, Yao X, Cox D, Wang H (2019) Mobile network intrusion detection for IoT system based on transfer learning algorithm. Clust Comput 22(4):9889–9904

    Article  Google Scholar 

  30. Kaivonen S, Ngai ECH (2020) Real-time air pollution monitoring with sensors on city bus. Digit Commun Netw 6(1):23–30

    Article  Google Scholar 

  31. Que S, Awuah-Offei K, Demirel A, Wang L, Demirel N, Chen Y (2019) Comparative study of factors affecting public acceptance of mining projects: evidence from USA, China and Turkey. J Clean Prod 237:117634

    Article  Google Scholar 

Download references

Acknowledgements

Project funded by China Postdoctoral Science Foundation. Project funded by National Key R&D Program of China (No. 2017YFB0503604; No. 2017YFB0503801). Project funded by Electronic fence system project. Project funded by the Project (017/2018/A) of FDCT. Project funded by the Project of Macao Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lianbing Deng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11063-024-11603-2

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, D., Qin, B., Liu, W. et al. RETRACTED ARTICLE: A City Monitoring System Based on Real-Time Communication Interaction Module and Intelligent Visual Information Collection System. Neural Process Lett 53, 2501–2517 (2021). https://doi.org/10.1007/s11063-020-10325-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10325-5

Keywords

Navigation