Multi-view vehicle re-identification (Re-ID) aims to retrieve all images of a target vehicle from a large gallery where the vehicles are captured from non-overlapping cameras. However, the drastic variation in vehicle appearance under different viewpoints greatly affects the performance of the multi-view vehicle Re-ID model, so the key issue in multi-view vehicle Re-ID is learning an effective feature representation that is robust to both dramatic intra-class variability and small inter-class variability. To achieve this goal, we have proposed a multi-center metric learning framework for multi-view vehicle Re-ID. In our approach, we model latent views from vehicle visual appearance directly without any extra labels except ID. Firstly, we introduce several latent view clusters for a vehicle to model latent multi-view information and each view cluster has a learnable center. Then multi-view vehicle matching task can be transformed into two subproblems, cross-view matching and cross-target matching. Finally, an intra-class ranking loss with cross-view center constraint and a cross-class ranking loss with cross-vehicle center constraint are proposed to address the two subproblems, respectively. Extensive experimental evaluations on three widely used benchmarks show the superiority of the proposed framework in contrast to a series of existing state-of-the-arts.

中文翻译：

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利用多中心度量学习对潜在视图进行建模以进行车辆重新识别

多视图车辆重新识别（Re-ID）的目的是从大型画廊检索目标车辆的所有图像，在大型画廊中，车辆是从非重叠摄像机捕获的。但是，不同视角下车辆外观的剧烈变化极大地影响了多视图车辆Re-ID模型的性能，因此多视图车辆Re-ID的关键问题是学习一种有效的特征表示方法，该方法对于两种场景都具有鲁棒性类内变异性和小类间变异性。为了实现这一目标，我们提出了用于多视图车辆Re-ID的多中心度量学习框架。在我们的方法中，我们直接根据车辆的外观为潜在视图建模，除了ID外没有任何其他标签。首先，我们为车辆引入了几个潜在视图集群，以对潜在的多视图信息进行建模，并且每个视图集群都有一个可学习的中心。然后，多视图车辆匹配任务可以转换为两个子问题，即交叉视图匹配和交叉目标匹配。最后，提出了具有交叉视角中心约束的类内排名损失和具有跨车辆中心约束的类内排名损失，分别解决了这两个子问题。在三个广泛使用的基准上进行的广泛实验评估表明，与一系列现有的最新技术相比，该框架的优越性。提出了一种具有交叉视角中心约束的类内排名损失和具有跨车辆中心约束的跨类排名损失来分别解决这两个子问题。在三个广泛使用的基准上进行的广泛实验评估表明，与一系列现有的最新技术相比，该框架的优越性。提出了一种具有交叉视角中心约束的类内排名损失和具有跨车辆中心约束的跨类排名损失来分别解决这两个子问题。在三个广泛使用的基准上进行的广泛实验评估表明，与一系列现有的最新技术相比，该框架的优越性。