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T2RIAD: A Two-Stage Framework for Truck Re-ID With Domain Adversarial and Distillation Learning

Zhenxuan Zeng,Jiangtao Ren

Published 2026 in IEEE transactions on intelligent transportation systems (Print)

ABSTRACT

Vehicle re-identification(Re-ID) is a critical task in Intelligent Transportation Systems, which aims to accurately identify vehicles across different viewpoints and time through visual analysis. However, existing Re-ID methods often struggle in truck scenarios due to the heterogeneity between the truck front and carriage features, dynamic carriage changes, and missing carriage information. To address these issues, we propose a two-stage truck re-identification framework based on domain adversarial learning and distillation learning (T2RIAD). The framework adopts a two-stage training strategy. In the first stage, for known carriage types, domain adversarial learning is employed to suppress the interference of dynamic changes in carriage states. Meanwhile, multi-teacher distillation is used to align and unify feature distributions across different carriage types by enforcing local similarity distillation and global logit consistency distillation, thereby guiding the student model to learn more stable and discriminative feature representations. In the second stage, for unknown carriage type, a dynamic cross-type sampling mechanism is introduced to enhance the generalization ability of the student model. Extensive experiments on two public datasets demonstrate the superiority of our T2RIAD over other state-of-the-art methods.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE transactions on intelligent transportation systems (Print)

  • Publication date

    2026-01-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/TITS.2025.3629354
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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