Abstract
Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with the similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i.e., camera view, vehicle type and vehicle color). Moreover, to overcome the shortcomings of limited vehicle images of different views, we design a view-specified generative adversarial network to generate the multi-view vehicle images. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID.
Abstract (translated)
由于其在智能城市和城市监控领域的潜在应用,车辆识别技术近年来受到了广泛关注。然而,由于视场的变化和光照的变化,它存在着较大的类内差异,特别是对于具有相似外观的不同身份,它也存在着类间相似性。为了解决这些问题,本文提出了一种新的深度网络体系结构,该体系结构以有意义的属性为指导,包括摄像机视图、车辆类型和车辆识别的颜色,特别是我们的网络是端到端的训练,包含三个子网络的深度特征嵌入相应的属性(即摄像机视图,车辆类型和车辆颜色)。此外,为了克服不同视点车辆图像有限的缺点,我们设计了一个视点特定的生成对抗网络来生成多视点车辆图像。对于网络培训,我们注释了Veri-776数据集上的视图标签。注意,我们可以直接在其他只有ID信息的数据集上采用预先训练的视图(以及类型和颜色)子网络,这表明我们的模型是通用的。在基准数据集Veri-776和VehicleID上进行的大量实验表明,所提出的方法取得了良好的性能,并产生了一种新的最先进的车辆识别技术。
URL
https://arxiv.org/abs/1905.08997