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RETRACTED ARTICLE: Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm

  • S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
  • Published:
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This article was retracted on 13 December 2022

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Abstract

Traffic sign recognition and lane detection play an important role in traffic flow planning, avoiding traffic accidents, and alleviating traffic chaos. At present, the traffic intelligent recognition rate still needs to be improved. In view of this, based on the neural network algorithm, this study constructs an intelligent transportation system based on neural network algorithm, and combines machine vision technology to carry out intelligent monitoring and intelligent diagnosis of traffic system. In addition, this study discusses in detail the core of the monitoring system: multi-target tracking algorithm, and introduces the complete implementation process and details of the system, and highlights the implementation and tracking effect of the multi-target tracker. Finally, this study uses case identification to analyze the effectiveness of the algorithm proposed by this paper. The research results show that the proposed method has certain practical effects and can be used as a reference for subsequent system construction.

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Authors and Affiliations

  1. School of Economics and Management, Northeast Electric Power University, Jilin, 132012, China

    Yantao Wang & Quan Wang

  2. State Grid Tianjin Electric Power Company, Tianjin, 300010, China

    Daxiang Suo

  3. State Grid Beijing Electric Power Company, Beijing, 100031, China

    Tiezheng Wang

Authors
  1. Yantao Wang
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  2. Quan Wang
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  3. Daxiang Suo
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  4. Tiezheng Wang
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Corresponding author

Correspondence to Yantao Wang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08149-6"

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Wang, Y., Wang, Q., Suo, D. et al. RETRACTED ARTICLE: Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm. Neural Comput & Applic 33, 8107–8117 (2021). https://doi.org/10.1007/s00521-020-04899-3

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  • DOI: https://doi.org/10.1007/s00521-020-04899-3

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