Group re-identification (GReID) aims to correctly associate images containing the same group members captured with non-overlapping camera networks, which has important applications in video surveillance. Unlike the person re-identification, the unique challenge of GReID lies in variations of group structure, including the number and layout of members. Current methods use certainty modeling, in which the specific group structure presented in each image is considered. However, certainty modeling can only describe finite group structures and shows poor generalization for unseen group structures, i.e., group variations that do not exist in the training set. In this paper, we propose a methodology called uncertainty modeling, which excavates near-infinite group structures from finite samples by simulating variations in both number and layout. Specifically, member uncertainty treats the number of intra-group members as a truncated Gaussian distribution instead of a fixed value and then simulates member variations by dynamic sampling. Layout uncertainty constructs random affine transformations about the positions of members to enlarge the fixed schemes in the training set. To implement the proposed methodology, we technically propose an Uncertainty-Modeling Second-Order Transformer (UMSOT) that extracts a first-order token for each member and further uses these tokens to learn a second-order token as a group feature. The UMSOT exploits the structural advantages of the transformer to explicitly extract layout features and efficiently integrate appearance and layout features, which are hardly achievable by current CNN- and GNN-based methods. Comprehensive experiments on four datasets (CSG, SYSUGroup, RoadGroup, and iLIDS-MCTS), fully demonstrate the superiority of the proposed method, which surprisingly outperforms the state-of-the-art method by 30.4% in Rank1 on the CSG dataset. https://github.com/LinlyAC/UMSOT.

群体重新识别（GReID）旨在正确关联使用非重叠摄像机网络捕获的包含相同群体成员的图像，这在视频监控中具有重要应用。与行人重识别不同，GReID 的独特挑战在于群体结构的变化，包括成员的数量和布局。当前的方法使用确定性建模，其中考虑每个图像中呈现的特定组结构。然而，确定性建模只能描述有限的群结构，并且对于不可见的群结构（即训练集中不存在的群变化）表现出较差的泛化能力。在本文中，我们提出了一种称为不确定性建模的方法，该方法通过模拟数量和布局的变化从有限样本中挖掘近乎无限的组结构。具体来说，成员不确定性将组内成员的数量视为截断高斯分布而不是固定值，然后通过动态采样来模拟成员变化。布局不确定性构造关于成员位置的随机仿射变换，以扩大训练集中的固定方案。为了实现所提出的方法，我们在技术上提出了一种不确定性建模二阶变换器（UMSOT），它为每个成员提取一阶令牌，并进一步使用这些令牌来学习作为一个组的二阶令牌特征。UMSOT 利用变压器的结构优势来显式提取布局特征并有效地集成外观和布局特征，这是当前基于 CNN 和 GNN 的方法难以实现的。在四个数据集（CSG、SYSUGroup、RoadGroup和iLIDS-MCTS）上的综合实验充分证明了所提出方法的优越性，在CSG数据集的Rank1中令人惊讶地比最先进的方法高出30.4%。https://github.com/LinlyAC/UMSOT。