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

Published in ACM Multimedia, 2018

Recommended citation: Hao Xiao, Weiyao Lin, Bin Sheng, Ke Lu, Junchi Yan, Jingdong Wang, Errui Ding, Yihao Zhang, and Hongkai Xiong. 2018. Group Re-Identification: Leveraging and Integrating Multi-Grain Information. In 2018 ACM Multimedia Conference

[Paper], [Slides], [Poster]


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.


This figure illustrates the overview of our proposed approach. Given the probe group image captured from one camera, our goal is to find the matched group images from a set of gallery group images captured from another camera. We represent each group image by a set of multi-grain objects, and extract the features for the multi-grain objects. The matching process is an iterative process. We compute the static and dynamic importance weights of multi-grain objects for the probe and gallery images according to the intermediate matching results. Then, we use a multi-order matching algorithm to compute intermediate matching results, which are used to update the dynamic importance weights. We perform the two stages iteratively, and obtain the final matching results.

Multi-grain Representation and Importance Weighting

Figure 1: (a) Illustration of matched-people sets and their distributions in the feature space (The color solid arrows indicate the one-to-one mapping results between individuals. People circled by the same color rectangles in camera B are matched to the same person in A, and belong to the same matched people set). (b) The derived importance weights for multi-grain objects (individuals, 2-people subgroup s, 3-people subgroups) in two group images. Note: the importance weights for some 2-people/3-people subgroups are not displayed in order for a cleaner illustration. (Best viewed in color)

Multi-order Matching

Figure 2: Illustration of multi-order association graph. Left: A cross-view group pair being matched; Right: The multi-order association graph constructed for the group pair.


We perform experiments on three datasets: (1) the public i-LID MCTS dataset which contains 274 group images for 64 groups; (2) our own constructed DukeMTMC Group dataset which includes 177 group image pairs extracted from a 8-camera-view DukeMTMC dataset; (3) our own constructed Road Group dataset which includes 162 group pairs taken from a two-camera crowd road scene. When constructing our own datasets, we automatically detect groups, and randomly select groups with different sizes & variations as the target groups in our dataset. Moreover, we define two cross-view groups as the same group when they have more than 60% members in common.

Figure 3: CMC results of Group Re-ID on different datasets. We compares our approach with the state-of-the-art group re-identification methods on different datasets: CRRRO-BRO, Covariance, PREF, BSC+CM. To further demonstrate the effectiveness of our approach, we also include the results of two state-of-the-art methods designed for single person Re-ID, which utilize patch saliency or a KMFA(Rχ2) distance metric to calculate image-wise similarity (Saliency and Mirror+KMFA). From this table, we can observe that our approach has better results than the existing group Re-ID methods. This demonstrates the effectiveness of our approach.