  The task of re-identifying groups of people underdifferent camera views is an
important yet less-studied problem.Group re-identification (Re-ID) is a very
challenging task sinceit is not only adversely affected by common issues in
traditionalsingle object Re-ID problems such as viewpoint and human
posevariations, but it also suffers from changes in group layout andgroup
membership. In this paper, we propose a novel conceptof group granularity by
characterizing a group image by multi-grained objects: individual persons and
sub-groups of two andthree people within a group. To achieve robust group
Re-ID,we first introduce multi-grained representations which can beextracted
via the development of two separate schemes, i.e. onewith hand-crafted
descriptors and another with deep neuralnetworks. The proposed representation
seeks to characterize bothappearance and spatial relations of multi-grained
objects, and isfurther equipped with importance weights which capture
varia-tions in intra-group dynamics. Optimal group-wise matching isfacilitated
by a multi-order matching process which in turn,dynamically updates the
importance weights in iterative fashion.We evaluated on three multi-camera
group datasets containingcomplex scenarios and large dynamics, with
experimental resultsdemonstrating the effectiveness of our approach. The
published dataset can be found in
\url{http://min.sjtu.edu.cn/lwydemo/GroupReID.html}
