Skip to main content
Erschienen in: Neural Computing and Applications 14/2021

14.04.2020 | S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications

RETRACTED ARTICLE: Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm

verfasst von: Yantao Wang, Quan Wang, Daxiang Suo, Tiezheng Wang

Erschienen in: Neural Computing and Applications | Ausgabe 14/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Albisser M, Dobre S, Berner C et al (2017) Aerodynamic coefficient identification of a space vehicle from multiple free-flight tests. J Spacecr Rockets 54(2):1–10CrossRef Albisser M, Dobre S, Berner C et al (2017) Aerodynamic coefficient identification of a space vehicle from multiple free-flight tests. J Spacecr Rockets 54(2):1–10CrossRef
2.
Zurück zum Zitat Huang L, Beauchemin D (2017) Forensic analysis of automotive paint chips for the identification of the vehicle manufacturer, colour and year of production using electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry. J Anal At Spectrom 32:1601–1607CrossRef Huang L, Beauchemin D (2017) Forensic analysis of automotive paint chips for the identification of the vehicle manufacturer, colour and year of production using electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry. J Anal At Spectrom 32:1601–1607CrossRef
3.
Zurück zum Zitat Liu P, Jilai YU (2017) Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet. J Mod Power Syst Clean Energy 6(3):1–15 Liu P, Jilai YU (2017) Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet. J Mod Power Syst Clean Energy 6(3):1–15
4.
Zurück zum Zitat Fotouhi A, Auger D, Propp K et al (2017) Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies. IET Power Electron 10(11):1289–1297CrossRef Fotouhi A, Auger D, Propp K et al (2017) Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies. IET Power Electron 10(11):1289–1297CrossRef
5.
Zurück zum Zitat Gentili M, Mirchandani PB (2018) Review of optimal sensor location models for travel time estimation. Transp Res C Emerg Technol 90:74–96CrossRef Gentili M, Mirchandani PB (2018) Review of optimal sensor location models for travel time estimation. Transp Res C Emerg Technol 90:74–96CrossRef
6.
Zurück zum Zitat Ya-Qin G, Hong-Li W, Sheng-Ao J et al (2018) Chemical characterization, spatialdistribution, and source identification of organic matter in PM_(2.5) in summertime Shanghai. China Environ Sci 158:66–78 Ya-Qin G, Hong-Li W, Sheng-Ao J et al (2018) Chemical characterization, spatialdistribution, and source identification of organic matter in PM_(2.5) in summertime Shanghai. China Environ Sci 158:66–78
7.
Zurück zum Zitat Allen J, Ghoreyshi M (2018) Forced motions design for aerodynamic identification and modeling of a generic missile configuration. Aerosp Sci Technol 77:742–754CrossRef Allen J, Ghoreyshi M (2018) Forced motions design for aerodynamic identification and modeling of a generic missile configuration. Aerosp Sci Technol 77:742–754CrossRef
8.
Zurück zum Zitat Armanini SF, De Visser CC, De Croon G et al (2016) Time-varying model identification of flapping-wing vehicle dynamics using flight data. J Guid Control Dyn 38(12):526–541CrossRef Armanini SF, De Visser CC, De Croon G et al (2016) Time-varying model identification of flapping-wing vehicle dynamics using flight data. J Guid Control Dyn 38(12):526–541CrossRef
9.
Zurück zum Zitat Farroni F, Lamberti R, Mancinelli N et al (2018) TRIP-ID: a tool for a smart and interactive identification of magic formula tyre model parameters from experimental data acquired on track or test rig. Mech Syst Signal Process 102:1–22CrossRef Farroni F, Lamberti R, Mancinelli N et al (2018) TRIP-ID: a tool for a smart and interactive identification of magic formula tyre model parameters from experimental data acquired on track or test rig. Mech Syst Signal Process 102:1–22CrossRef
10.
Zurück zum Zitat Bao Y, Shi Z, Beck JL et al (2017) Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations. Struct Control Health Monit 24(3):e1889CrossRef Bao Y, Shi Z, Beck JL et al (2017) Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations. Struct Control Health Monit 24(3):e1889CrossRef
12.
Zurück zum Zitat Bai Y, Lou Y, Gao F et al (2018) Group sensitive triplet embedding for vehicle re-identification. IEEE Trans Multimed 20:2385–2399CrossRef Bai Y, Lou Y, Gao F et al (2018) Group sensitive triplet embedding for vehicle re-identification. IEEE Trans Multimed 20:2385–2399CrossRef
13.
Zurück zum Zitat Liu X, Liu W, Mei T et al (2017) PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20:645–658CrossRef Liu X, Liu W, Mei T et al (2017) PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20:645–658CrossRef
14.
Zurück zum Zitat Yuan GP, Tang YP, Han WM et al (2018) Vehicle category recognition based on deep convolutional neural network. J Zhejiang Univ (Eng Sci) 52:694–702 Yuan GP, Tang YP, Han WM et al (2018) Vehicle category recognition based on deep convolutional neural network. J Zhejiang Univ (Eng Sci) 52:694–702
15.
Zurück zum Zitat van Wyk F, Wang Y, Khojandi A et al (2019) Real-time sensor anomaly detection and identification in automated vehicles. IEEE Trans Intell Transp Syst 21(3):1264–1276 van Wyk F, Wang Y, Khojandi A et al (2019) Real-time sensor anomaly detection and identification in automated vehicles. IEEE Trans Intell Transp Syst 21(3):1264–1276
16.
Zurück zum Zitat Cheng HY, Yu CC, Lin CL et al (2019) Ego-lane position identification with event warning applications. IEEE Access 7:14378–14386CrossRef Cheng HY, Yu CC, Lin CL et al (2019) Ego-lane position identification with event warning applications. IEEE Access 7:14378–14386CrossRef
17.
Zurück zum Zitat Kan S, Cen Y, He Z et al (2019) Supervised deep feature embedding with hand crafted feature. IEEE Trans Image Process 28(12):5809–5823MathSciNetCrossRefMATH Kan S, Cen Y, He Z et al (2019) Supervised deep feature embedding with hand crafted feature. IEEE Trans Image Process 28(12):5809–5823MathSciNetCrossRefMATH
18.
Zurück zum Zitat Coutinho WP, Battarra M, Fliege J (2018) The unmanned aerial vehicle routing and trajectory optimisation problem. Comput Ind Eng 120:116–128CrossRef Coutinho WP, Battarra M, Fliege J (2018) The unmanned aerial vehicle routing and trajectory optimisation problem. Comput Ind Eng 120:116–128CrossRef
19.
Zurück zum Zitat Li D, Zhongbin N, Xinyun W (2018) Application of convolution neural network in vehicle identification system. J Anhui Univ Technol (Nat Sci) 35(02):148–152 Li D, Zhongbin N, Xinyun W (2018) Application of convolution neural network in vehicle identification system. J Anhui Univ Technol (Nat Sci) 35(02):148–152
20.
Zurück zum Zitat Wang Y, Bialkowski KS, Pretorius AJ et al (2017) In-road microwave sensor for electronic vehicle identification and tracking: link budget analysis and antenna prototype. IEEE Trans Intell Transp Syst 19:123–128CrossRef Wang Y, Bialkowski KS, Pretorius AJ et al (2017) In-road microwave sensor for electronic vehicle identification and tracking: link budget analysis and antenna prototype. IEEE Trans Intell Transp Syst 19:123–128CrossRef
21.
Zurück zum Zitat Zhu J, Zeng H, Jin X et al (2019) Joint horizontal and vertical deep learning feature for vehicle re-identification. Sci China Inf Sci 62(9):199101CrossRef Zhu J, Zeng H, Jin X et al (2019) Joint horizontal and vertical deep learning feature for vehicle re-identification. Sci China Inf Sci 62(9):199101CrossRef
22.
Zurück zum Zitat Qian W, Yimin C, Youdong D (2018) Vehicle re-identification algorithm based on bag of visual words in complicated environments. J Comput Appl 38(05):1299–1303 Qian W, Yimin C, Youdong D (2018) Vehicle re-identification algorithm based on bag of visual words in complicated environments. J Comput Appl 38(05):1299–1303
23.
Zurück zum Zitat Liu Y, MacDonald J, Di Maio D (2019) Modal parameter identification from measurements of vehicle-bridge interaction. In: Pakzad S (ed) Dynamics of civil structures, vol 2. Conference proceedings of the society for experimental mechanics series. Springer, Cham, pp 247–249CrossRef Liu Y, MacDonald J, Di Maio D (2019) Modal parameter identification from measurements of vehicle-bridge interaction. In: Pakzad S (ed) Dynamics of civil structures, vol 2. Conference proceedings of the society for experimental mechanics series. Springer, Cham, pp 247–249CrossRef
24.
Zurück zum Zitat Huang Z, Qiu S, Li J et al (2018) Road traffic sign identification in weak illumination for intelligent vehicle based on machine vision. Recent Pat Mech Eng 11:127–134CrossRef Huang Z, Qiu S, Li J et al (2018) Road traffic sign identification in weak illumination for intelligent vehicle based on machine vision. Recent Pat Mech Eng 11:127–134CrossRef
Metadaten
Titel
RETRACTED ARTICLE: Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm
verfasst von
Yantao Wang
Quan Wang
Daxiang Suo
Tiezheng Wang
Publikationsdatum
14.04.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04899-3

Weitere Artikel der Ausgabe 14/2021

Neural Computing and Applications 14/2021 Zur Ausgabe