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<p>Cross-View Truck Re-Identification (CV-TR) constitutes a pivotal component in intelligent urban traffic systems, particularly under non-cooperative surveillance scenarios where license plate information or aerial imagery becomes inaccessible. This specialized visual intelligence task exhibits two fundamental technical barriers that diverge fundamentally from general object re-identification (Re-ID): 1) Fine-grained discrimination, which required between visually similar truck subtypes, and 2) Single-view, which limitations in capturing multi-angle features critical for robust cross-camera matching. To bridge this gap, we propose two key contributions: First, we introduce a CV-TR dataset, a large-scale benchmark comprising 62,783 annotated truck images collected from three real-world traffic surveillance scenarios. It includes 2,683 unique truck identities, each documented from front, side, and rear viewpoints. Second, we develop the Dynamic Adaptive Cross-View Network (DACVNet), which features a vehicle-centric architecture with adaptive feature fusion attention mechanism to tackle large-scale cross-view matching challenges. Experiments show DACVNet achieves state-of-the-art performance, significantly surpassing existing approaches in cross-view truck Re-ID. The CV-TR dataset is available at <a href="https://pan.baidu.com/s/11LF0vIB7hCJQ9_rdpNNs2A?pwd=eqt7">https://pan.baidu.com/s/11LF0vIB7hCJQ9_rdpNNs2A?pwd=eqt7</a>.</p>

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A Large-Scale Cross-View Truck Benchmark with Dynamic Adaptive Fusion Attention Mechanism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Xue-Yan Wang,
  • Qiang Lv,
  • Zhi-Hua Li,
  • Qi-Rui Huang,
  • Si-Cheng Zhao,
  • Si-Bao Chen,
  • Jin Tang

摘要

Cross-View Truck Re-Identification (CV-TR) constitutes a pivotal component in intelligent urban traffic systems, particularly under non-cooperative surveillance scenarios where license plate information or aerial imagery becomes inaccessible. This specialized visual intelligence task exhibits two fundamental technical barriers that diverge fundamentally from general object re-identification (Re-ID): 1) Fine-grained discrimination, which required between visually similar truck subtypes, and 2) Single-view, which limitations in capturing multi-angle features critical for robust cross-camera matching. To bridge this gap, we propose two key contributions: First, we introduce a CV-TR dataset, a large-scale benchmark comprising 62,783 annotated truck images collected from three real-world traffic surveillance scenarios. It includes 2,683 unique truck identities, each documented from front, side, and rear viewpoints. Second, we develop the Dynamic Adaptive Cross-View Network (DACVNet), which features a vehicle-centric architecture with adaptive feature fusion attention mechanism to tackle large-scale cross-view matching challenges. Experiments show DACVNet achieves state-of-the-art performance, significantly surpassing existing approaches in cross-view truck Re-ID. The CV-TR dataset is available at https://pan.baidu.com/s/11LF0vIB7hCJQ9_rdpNNs2A?pwd=eqt7.