Group Re-Identification: Leveraging and Integrating Multi-Grain Information

This paper addresses an important yet less-studied problem: re-identifying groups of people in different camera views. Group re-identification (Re-ID) is very challenging since it is not only interfered by view-point and human pose variations in the traditional single-object Re-ID tasks, but also suffers from group layout and group member variations. To handle these issues, we propose to leverage the information of multi-grain objects: individual person and subgroups of two and three people inside a group image. We compute multi-grain representations to characterize the appearance and spatial features of multi-grain objects and evaluate the importance weight of each object for group Re-ID, so as to handle the interferences from group dynamics. We compute the optimal group-wise matching by using a multi-order matching process based on the multi-grain representation and importance weights. Furthermore, we dynamically update the importance weights according to the current matching results and then compute a new optimal group-wise matching. The two steps are iteratively conducted, yielding the final matching results. Experimental results on various datasets demonstrate the effectiveness of our approach.