  Object re-identification is of increasing importance in visual surveillance.
Most existing works focus on re-identify individual from multiple cameras while
the application of group re-identification (Re-ID) is rarely discussed. We
redefine Group Re-identification as a process which includes pedestrian
detection, feature extraction, graph model construction, and graph matching.
Group re-identification is very challenging since it is not only interfered by
view-point and human pose variations in the traditional re-identification
tasks, but also suffered from the challenges in group layout change and group
member variation. To address the above challenges, this paper introduces a
novel approach which leverages the multi-granularity information inside groups
to facilitate group re-identification. We first introduce a multi-granularity
Re-ID process, which derives features for multi-granularity objects
(people/people-subgroups) in a group and iteratively evaluates their
importances during group Re-ID, so as to handle group-wise misalignments due to
viewpoint change and group dynamics. We further introduce a multi-order
matching scheme. It adaptively selects representative people/people-subgroups
in each group and integrates the multi-granularity information from these
people/people-subgroups to obtain group-wise matching, hence achieving a more
reliable matching score between groups. Experimental results on various
datasets demonstrate the effectiveness of our approach.
