【摘要】 【目标】针对外观和颜色差异细微车辆之间难以区分的问题，提出了一种融合时空与全局上下文信息的车辆重识别算法，旨在提升智能交通场景中复杂条件下的检索与跟踪性能。【方法】该方法首先利用SE-block和自注意力机制提取图像全局特征，突出重要信息并抑制冗余特征。随后通过动态多层感知器(DMLP)在高维空间中自适应地融合图像特征与时空信息，动态生成权重实现非线性映射，从而提升特征的区分性。训练过程中结合三元组损失与交叉熵损失优化模型参数，以同时保证类间分离性和类内紧凑性。【结果】在公开数据集VeRi-776上的评估表明，该模型取得了81.9%的平均精度均值和96.5%的Rank-1准确率，较当前最优算法分别提升2.4%和0.9%。消融试验进一步验证了SE-block和DMLP在全局上下文建模与时空特征融合中的关键作用，证明其在对外观高度相似的车辆进行区分时具有显著优势。【结论】提出的算法通过融合时空与全局上下文信息能够有效增强车辆重识别的判别能力，为智能交通系统中跨摄像头目标检索和跟踪提供了可靠的技术支持，并为后续在更大规模和更复杂场景下的扩展应用奠定了基础。更多还原

【Abstract】 [Objective] To address the challenge of distinguishing vehicles with subtle differences in appearance and color,this study proposes a vehicle re-identification algorithm integrating spatio-temporal and global contextual information. It aims to enhance the retrieval and tracking performances in ITS scenarios and complex conditions. [Method] First,the algorithm employed SE-block and self-attention mechanism to extract global image features,highlighting critical information while suppressing redundant ones. Second,a dynamic multi-layer perceptron( DMLP) was used to adaptively fuse image features and spatio-temporal information in a high-dimensional space,where dynamic weights were generated for nonlinear mapping to improve discriminability. Finally,the model parameters were jointly optimized by using triplet loss and crossentropy loss during training,ensuring both inter-class separability and intra-class compactness. [Result]The experimental evaluation on dataset VeRi-776 shows that the model achieves a mean average precision of81. 9%,and a Rank-1 accuracy of 96. 5%,representing improvements of 2. 4% and 0. 9% over the best existing methods. Ablation studies further confirm the effectiveness of SE-block and DMLP in global context modeling and spatio-temporal feature fusion,demonstrating clear advantages in distinguishing vehicles with highly similar appearances. [Conclusion] The proposed algorithm effectively enhances the discriminative capability of vehicle re-identification through the integration of spatio-temporal and global contextual information. It provides a reliable support for cross-camera retrieval and tracking in intelligent transportation systems,and establishes a foundation for future applications in larger-scale and more complex scenarios.更多还原