The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China.
Sensors (Basel). 2020 Sep 9;20(18):5142. doi: 10.3390/s20185142.
Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.
车辆再识别在监控视频中的跨摄像机跟踪和车辆搜索中起着重要作用。由于不同摄像机拍摄的同一车辆的外观差异较大，同一型号的不同车辆之间的相似度较高，因此车辆再识别具有一定的挑战性。大多数现有方法使用中心代理来表示车辆身份;然而，类内方差使得将同一身份的图像拟合到一个中心特征变得非常困难，并且属于不同身份的具有高相似度的图像不能被有效分离。在本文中，我们提出了一种考虑不同视角的采样策略和一种多代理约束损失函数，该函数使用多个代理来表示一个类别，并对来自不同视角的同一车辆的图像施加不同的约束。我们提出的采样策略有助于更好地利用相机信息在小批量中挖掘与不同代理对应的样本。多代理约束损失函数将图像推向同一类的最远代理，并将来自不同类的最近代理的图像推得更远，从而在决策边界之间产生更大的间隔。在两个大规模车辆数据集（VeRi 和 VehicleID）上的广泛实验表明，我们使用单分支网络学习的全局特征优于具有更复杂网络的先前工作，以及那些进一步利用时空信息重新排序的工作。此外，我们的方法易于插入到其他分类方法中，以提高性能。
