Person re-identification is a standard and established problem in the computer vision community. In recent years, vehicle re-identification is also getting more attention. In this paper, we focus on both these tasks and propose a method for aggregation of features in temporal domain as it is common to have multiple observations of the same object. The aggregation is based on weighting different elements of the feature vectors by different weights and it is trained in an end-to-end manner by a Siamese network. The experimental results show that our method outperforms other existing methods for feature aggregation in temporal domain on both vehicle and person re-identification tasks. Furthermore, to push research in vehicle re-identification further, we introduce a novel dataset CarsReId74k. The dataset is not limited to frontal/rear viewpoints. It contains 17,681 unique vehicles, 73,976 observed tracks, and 277,236 positive pairs. The dataset was captured by 66 cameras from various angles.

人员重新识别是计算机视觉社区中的一个标准且已确定的问题。近年来，车辆的重新识别也越来越受到关注。在本文中，我们专注于这两个任务，并提出了一种在时域中进行特征聚合的方法，因为对同一物体进行多次观测是很常见的。聚合基于对特征向量的不同元素进行不同的加权，并由暹罗网络以端到端的方式进行训练。实验结果表明，在车辆和人员重新识别任务上，我们的方法在时域方面均优于其他现有方法。此外，为了进一步推动车辆重新识别的研究，我们引入了一个新的数据集CarsReId74k。数据集不限于正面/背面视点。它包含17,681辆独特的车辆，73,976条观察到的赛道和277,236条阳性对。该数据集由66个摄像机从各个角度捕获。