  Person re-identification is best known as the problem of associating a single
person that is observed from one or more disjoint cameras. The existing
literature has mainly addressed such an issue, neglecting the fact that people
usually move in groups, like in crowded scenarios. We believe that the
additional information carried by neighboring individuals provides a relevant
visual context that can be exploited to obtain a more robust match of single
persons within the group. Despite this, re-identifying groups of people
compound the common single person re-identification problems by introducing
changes in the relative position of persons within the group and severe
self-occlusions. In this paper, we propose a solution for group
re-identification that grounds on transferring knowledge from single person
re-identification to group re-identification by exploiting sparse dictionary
learning. First, a dictionary of sparse atoms is learned using patches
extracted from single person images. Then, the learned dictionary is exploited
to obtain a sparsity-driven residual group representation, which is finally
matched to perform the re-identification. Extensive experiments on the i-LIDS
groups and two newly collected datasets show that the proposed solution
outperforms state-of-the-art approaches.
