Journal of System Simulation
Abstract
Abstract: Due to factors such as viewpoint changes, complex environments, and pose differences under multiple cameras, the images of the same vehicle in different scenes show huge appearance ambiguity, which brings challenges to vehicle identity matching. In order to solve this problem, a feature robustness enhancement method for vehicle recognition is proposed under the transformer framework. Based on the fact that the structural information of the vehicle is invariant under multiple cameras, a module for enhancing structural information guided by contour features is designed, and a structural feature perception loss is proposed to promote the fusion of structural information in the model. The attribute information of the vehicle is embedded into the transformer framework as a vector, which further alleviates the influence of vehicle pose changes under multiple viewpoints. Experimental results show that the proposed method exhibits superiority and better recognition effect compared with similar methods on VeRi-776 and VehicleID datasets.
Recommended Citation
Luo, Huicheng and Wang, Shujuan
(2023)
"Multi-camera Vehicle Recognition Method Based on Feature Robustness Enhancement,"
Journal of System Simulation: Vol. 35:
Iss.
5, Article 14.
DOI: 10.16182/j.issn1004731x.joss.22-0069
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss5/14
First Page
1059
Revised Date
2022-03-21
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.22-0069
Last Page
1074
CLC
TP391.9
Recommended Citation
Huicheng Luo, Shujuan Wang. Multi-camera Vehicle Recognition Method Based on Feature Robustness Enhancement[J]. Journal of System Simulation, 2023, 35(5): 1059-1074.
DOI
10.16182/j.issn1004731x.joss.22-0069
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