Vehicle re-identification (reID) aims to search the target vehicle in a non-overlapping multi-camera network, which is important for the intelligent analysis in large scale of surveillance videos. Many existing methods have employed various techniques to achieve discriminative information. However, those methods always focus on the description of one view for the same vehicle images. Hence, a generated multiple sparse information fusion method is proposed for exploiting latent features from multi-views, which employs three different deep networks to extract multiple features from coarse to fine. And these features are regarded as multi-view features. Besides, to fuse these features reasonably, the paper transfers various features into a common space for better seeking distinctive features. Especially, besides ResNet, two feature learning networks are proposed to learn different features, respectively. One is designed to learn robust feature by dropping some features randomly when training the reID model. Another is to combine various salient features from different layers, which forms strong features for the reID task. Moreover, comprehensive experimental results have demonstrated that our proposed method can achieve competitive performances on benchmark datasets VehicleID and VeRi-776.

车辆重新识别（reID）旨在在非重叠的多摄像头网络中搜索目标车辆，这对于大规模监控视频的智能分析具有重要意义。许多现有方法采用了各种技术来实现判别信息。然而，这些方法总是专注于对相同车辆图像的一个视图的描述。因此，提出了一种生成的多稀疏信息融合方法来利用多视图中的潜在特征，该方法采用三种不同的深度网络从粗到细提取多个特征。而这些特征被认为是多视图特征。此外，为了合理融合这些特征，本文将各种特征转移到一个公共空间中，以便更好地寻找与众不同的特征。特别是，除了 ResNet，提出了两个特征学习网络来分别学习不同的特征。一种旨在通过在训练 reID 模型时随机删除一些特征来学习鲁棒特征。另一个是结合来自不同层的各种显着特征，为 reID 任务形成强大的特征。此外，综合实验结果表明，我们提出的方法可以在基准数据集 VehicleID 和 VeRi-776 上实现具有竞争力的性能。