Part Relational Mean Model for Group Re-Identification

Most current research on pedestrian re-identification (ReID) is focusing on single-person ReID. However, people are rarely alone and often walk together in groups. Therefore, there is an urgent need to study the problem of group ReID (G-ReID). G-ReID is challenging because of the difficulties related to the differences in group appearance caused by changes in the group layout and membership. In this paper, we have proposed a part-based minus-average relational and arithmetic mean descriptor (PRM) algorithm to obtain a robust representation of groups. Based on local features, we have designed the arithmetic mean descriptor and the minus-average relational descriptor to solve the G-ReID problem caused by changes in the number of group members and their relative positions within the group. Moreover, the minus-average relational descriptor can also be used to describe the differences in the appearance of group members. Considering the rarity of G-ReID datasets and the need to improve the applicability of the G-ReID algorithm in real scenarios, we have collected a new dataset called the Bus Rapid Transit (BRT) G-ReID dataset. Extensive experimental results demonstrate the effectiveness of the PRM algorithm and indicate that it outperforms state-of-the-art algorithms by 7.5% for the cumulative matching feature (CMC-1) on the i-LIDS MCTS group dataset and by 19.4% for the CMC-1 on the Road Group dataset and it outperforms the baseline by 2.4% for the CMC-1 on the BRT dataset.