Group Re-Identification with Multi-grained Matching and Integration
This addresses the less-studied challenge of re-identifying groups of people across camera views, which is important for surveillance and security applications, but the work appears incremental as it builds on existing single object Re-ID methods.
The paper tackles the problem of group re-identification (Re-ID) by proposing a multi-grained matching and integration approach, achieving effective results on three multi-camera group datasets with complex scenarios and large dynamics.
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}