[1]YANG Yuting,MIAO Duoqian.Person search algorithm based on multi-granularity matching[J].CAAI Transactions on Intelligent Systems,2022,17(2):420-426.[doi:10.11992/tis.202105038]
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Person search algorithm based on multi-granularity matching

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 420-426 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-03-05
Title:
Person search algorithm based on multi-granularity matching
Author(s):
YANG Yuting1;  MIAO Duoqian2
1. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China;
2. Key Laboratory of Embedded System and Service Computing Ministry of Education, Tongji University, Shanghai 201804, China
Keywords:
person search; person detection; person re-identification; multi-granularity; multi-feature fusion; deep learning; robustness; computer vision
CLC:
TP389.1
DOI:
10.11992/tis.202105038
Abstract:
Person search aims to locate and recognize a specified person from a series of uncropped images, which combines Pedestrian Detection and Person Re-identification (re-ID). Existing methods based on Faster R-CNN have been widely used to solve the two subtasks jointly. However, the optimization goals of the two subtasks are inconsistent. To alleviate this issue, we propose a dual global pooling structure, which applies Global Average Pooling to extract common features in detection branch and applies Global K-Max Pooling to extract discriminative features in re-ID branch. In this way, our method successfully extracts features that conform to the granularity characteristics of the two subtasks. In addition, to relieve the granularity mismatch problem, we propose a multi-granularity gallery boxes re-weighting algorithm, which incorporates granularity difference into similarity measurement. Extensive experiments show that our method greatly improves the performance of the end-to-end framework on two widely used person search datasets, CUHK-SYSU and PRW.
References:
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