  Learning to re-identify or retrieve a group of people across non-overlapped
camera systems has important applications in video surveillance. However, most
existing methods focus on (single) person re-identification (re-id), ignoring
the fact that people often walk in groups in real scenarios. In this work, we
take a step further and consider employing context information for identifying
groups of people, i.e., group re-id. We propose a novel unified framework based
on graph neural networks to simultaneously address the group-based re-id tasks,
i.e., group re-id and group-aware person re-id. Specifically, we construct a
context graph with group members as its nodes to exploit dependencies among
different people. A multi-level attention mechanism is developed to formulate
both intra-group and inter-group context, with an additional self-attention
module for robust graph-level representations by attentively aggregating
node-level features. The proposed model can be directly generalized to tackle
group-aware person re-id using node-level representations. Meanwhile, to
facilitate the deployment of deep learning models on these tasks, we build a
new group re-id dataset that contains more than 3.8K images with 1.5K annotated
groups, an order of magnitude larger than existing group re-id datasets.
Extensive experiments on the novel dataset as well as three existing datasets
clearly demonstrate the effectiveness of the proposed framework for both
group-based re-id tasks. The code is available at
https://github.com/daodaofr/group_reid.
