Group re-identification aims to match groups of people across disjoint cameras. In this task, the contextual information from neighbor individuals can be exploited for re-identifying each individual within the group as well as the entire group. However, compared with single person re-identification, it brings new challenges including group layout and group membership changes. Motivated by the observation that individuals who are close together are more likely to keep in the same group under different cameras than those who are far apart, we propose to model each group as a spatial K-nearest neighbor graph (SKNNG) and design a group context graph neural network (GCGNN) for graph representation learning. Specifically, for each node in the graph, the proposed GCGNN learns an embedding which aggregates the contextual information from neighbor nodes. We design multiple weighting kernels for neighborhood aggregation based on the graph properties including node in-degrees and spatial relationship attributes. We compute the similarity scores between node embeddings of two graphs for group member association and obtain the matching score between the two graphs by summing up the similarity scores of all linked node pairs. Experimental results on three public datasets show that our approach performs favorably against state-of-the-art methods and achieves high efficiency.

In this paper, a hierarchical and informative summarization framework is proposed, which facilitates rapid video browsing. Moreover, a method for loitering detection is exploited to indicate potential abnormal behaviors. The hierarchical framework includes two levels: a holistic-level and an object-level. The holistic-level summarization provides viewers with a comprehensive and compact representation of the original video, while the object-level summarization extracts the narrative information of each object, including trajectory, direction, time, changes of appearance and indication of the loitering behavior. The two summarizations are formulated as two different energy minimization problems, which are solved by the proposed heuristic algorithms. Our framework is evaluated on two publicly available datasets. Experimental results demonstrate that the proposed method performs favourably in providing holistic- and object-level information, fast browsing, and loitering detection.