RETRACTED ARTICLE: Design of traffic object recognition system based on machine learning

In recent years, researchers have proposed many methods to solve the problem of obstacle detection. However, computer vision-based vehicle detection and recognition technology is still not mature enough. This research combines machine learning technology to construct a traffic object recognition sys...

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Veröffentlicht in:Neural computing & applications Jg. 33; H. 14; S. 8143 - 8156
Hauptverfasser: Li, Daming, Deng, Lianbing, Cai, Zhiming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.07.2021
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Probability and Statistics in Computer Science
S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications
Image recognition
Vehicle identification system
Machine learning
Traffic
ISSN:0941-0643, 1433-3058
Online-Zugang:Volltext
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Zusammenfassung:In recent years, researchers have proposed many methods to solve the problem of obstacle detection. However, computer vision-based vehicle detection and recognition technology is still not mature enough. This research combines machine learning technology to construct a traffic object recognition system and applies innovative technology to the computer vision recognition system to construct an automatic identification system suitable for current traffic demand and improve the stability of the traffic system. Moreover, this study uses a combination of a monocular camera and a binocular camera to sense the traffic environment and obtain vehicle position and velocity information. In addition, this study is based on the binocular stereo camera to find the obstacle space and obtain the obstacle relative to the position and speed of the vehicle and combine the obstacle space information to optimize the obstacle frame of the target vehicle. Through experimental research and analysis, it can be seen that the algorithm proposed in this study has certain recognition effect and can be applied to traffic object recognition.
Bibliographie:ObjectType-Correction/Retraction-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04912-9