Previous works on vehicle Re-ID mainly focus on extracting global features and learning distance metrics. Because some vehicles commonly share same model and maker, it is hard to distinguish them based on their global appearances. Compared with the global appearance, local regions such as decorations and inspection stickers attached to the windshield, may be more distinctive for vehicle Re-ID. To embed the detailed visual cues in those local regions, we propose a Region-Aware deep Model (RAM). Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions. As each local region conveys more distinctive visual cues, RAM encourages the deep model to learn discriminative features. We also introduce a novel learning algorithm to jointly use vehicle IDs, types/models, and colors to train the RAM. This strategy fuses more cues for training and results in more discriminative global and regional features. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.

提出了一种区域感知深度模型（RAM），不仅可以提取全局特征用于车辆Re-ID，更可以从一系列局部区域中提取特征，利用车辆ID、类型/型号和颜色，联合训练RAM，融合更多信息用于训练，获得更具区分度的特征，并在VeRi和VehicleID两个大规模车辆Re-ID数据集上得到了良好的实验效果。

RAM: 基于区域的深度模型的车辆再识别