Paper Info
Title
Cross-modal biometric fusion intelligent traffic recognition system combined with real-time data operation
Abstract
Intelligent traffic recognition system is the development direction of the future traffic system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology into the entire ground traffic management system. It establishes a real-time, accurate, and efficient integrated transportation management system that plays a role in a wide range and all directions. The aim of this article is to integrate cross-modal biometrics into an intelligent traffic recognition system combined with real-time data operations. Based on the cross-modal recognition algorithm, it can better re-identify the vehicle cross-modally by building a model. First, this article first presents a general introduction to the cross-modal recognition method. Then, the experimental analysis is conducted on the classification of vehicle images recognized by the intelligent transportation system, the complexity of vehicle logo recognition, and the recognition of vehicle images with different lights. Finally, the cross-modal recognition algorithm is introduced into the dynamic analysis of the intelligent traffic recognition system. The cross-modal traffic recognition system experiment is carried out. The experimental results show that the intraclass distribution loss function can improve the Rank 1 recognition rate and mAP value by 6-7% points on the basis of the baseline method. This shows that improving the modal invariance feature by reducing the distribution difference between different modal images of the same vehicle can effectively deal with the feature information imbalance caused by modal changes.
Year
DOI
Venue
2022
10.1515/comp-2022-0252
OPEN COMPUTER SCIENCE
Keywords
DocType
Volume
real-time data operations, cross-modal recognition, modal biometrics, intelligent transportation systems
Journal
12
Issue
ISSN
Citations 
1
2299-1093
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Wei Xu1411.47
Yujin Zhai200.34