Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera scenarios. However, the images of different vehicles may have small visual discrepancies when they have the same/similar attributes, e.g., the same/similar color, type, and manufacturer. Meanwhile, the images from a vehicle may have large visual discrepancies with different states, e.g., different camera views, vehicle viewpoints, and capture time. In this paper, we propose an attribute and state guided structural embedding network (ASSEN) to achieve discriminative feature learning by attribute-based enhancement and state-based weakening for vehicle Re-ID. First, we propose an attribute-based enhancement and expanding module to enhance the discrimination of vehicle features through identity-related attribute information, and we design an attribute-based expanding loss to increase the feature gap between different vehicles. Second, we design a state-based weakening and shrinking module, which not only weakens the state information that interferes with identification but also reduces the intra-class feature gap by a state-based shrinking loss. Third, we propose a global structural embedding module that exploits the attribute information and state information to explore hierarchical relationships between vehicle features, then we use these relationships for feature embedding to learn more robust vehicle features. Extensive experiments on benchmark datasets VeRi-776, VehicleID, and VERI-Wild demonstrate the superior performance and generalization of the proposed method against state-of-the-art vehicle Re-ID methods. The code is available at https://github.com/ttaalle/fast_assen.

中文翻译：

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用于车辆重新识别的属性和状态引导结构嵌入网络


车辆重新识别（Re-ID）是智慧城市和智能交通中的一项关键任务，旨在匹配非重叠监控摄像头场景中的车辆图像。然而，当不同车辆具有相同/相似的属性（例如相同/相似的颜色、类型和制造商）时，不同车辆的图像可能具有较小的视觉差异。同时，来自车辆的图像在不同状态下可能具有较大的视觉差异，例如不同的摄像机视图、车辆视点和捕获时间。在本文中，我们提出了一种属性和状态引导的结构嵌入网络（ASSEN），通过基于属性的增强和基于状态的弱化来实现车辆重新识别的判别性特征学习。首先，我们提出了一种基于属性的增强和扩展模块，通过与身份相关的属性信息来增强车辆特征的辨别力，并设计了一种基于属性的扩展损失来增加不同车辆之间的特征差距。其次，我们设计了一个基于状态的弱化和收缩模块，它不仅削弱了干扰识别的状态信息，而且通过基于状态的收缩损失减少了类内特征差距。第三，我们提出了一个全局结构嵌入模块，利用属性信息和状态信息来探索车辆特征之间的层次关系，然后使用这些关系进行特征嵌入来学习更鲁棒的车辆特征。在基准数据集 VeRi-776、VehicleID 和 VERI-Wild 上进行的大量实验证明了所提出的方法相对于最先进的车辆 Re-ID 方法具有优越的性能和泛化性。该代码可在 https://github.com/ttaalle/fast_assen 获取。