TANet: Text region attention learning for vehicle re-identification

Hu, Wenbo; Zhan, Hongjian; Shivakumara, Palaiahnakote; Pal, Umapada; Lu, Yue

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.

Citation

Hu, W., Zhan, H., Shivakumara, P., Pal, U., & Lu, Y. (2024). TANet: Text region attention learning for vehicle re-identification. Engineering Applications of Artificial Intelligence, 133, https://doi.org/10.1016/j.engappai.2024.108448

• Journal Article Type: Article
• Acceptance Date: Apr 11, 2024
• Online Publication Date: Apr 26, 2024
• Publication Date: 2024
• Deposit Date: Apr 26, 2024
• Publicly Available Date: Apr 27, 2026
• Journal: Engineering Applications of Artificial Intelligence
• Print ISSN: 0952-1976
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 133
• DOI: https://doi.org/10.1016/j.engappai.2024.108448