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TANet: Text region attention learning for vehicle re-identification.
- Source :
-
Engineering Applications of Artificial Intelligence . Jul2024:Part E, Vol. 133, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- In recent years, the challenge of distinguishing vehicles of the same model has prompted a shift towards leveraging both global appearances and local features, such as lighting and rearview mirrors, for vehicle re-identification (ReID). Despite advancements, accurately identifying vehicles remains complex, particularly due to the underutilization of highly discriminative text regions. This paper introduces the Text Region Attention Network (TANet), a novel approach that integrates global and local information with a specific focus on text regions for improved feature learning. TANet uniquely captures stable and distinctive features across various vehicle views, demonstrating its effectiveness through rigorous evaluation on the VeRi-776, VehicleID, and VERI-Wild datasets. TANet significantly outperforms existing methods, achieving mAP scores of 83.6% on VeRi-776, 84.4% on VehicleID (Large), and 76.6% on VERI-Wild (Large). Statistical tests further validate the superiority of TANet over the baseline, showcasing notable improvements in mAP and Top-1 through Top-15 accuracy metrics. [ABSTRACT FROM AUTHOR]
- Subjects :
- *REARVIEW mirrors
*TEXT recognition
*VEHICLE models
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 133
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 177749156
- Full Text :
- https://doi.org/10.1016/j.engappai.2024.108448