[1]WANG Luyao,WANG Fengsui,YAN Tao,et al.Cross-modal person re-identification combining multi-scale features and confusion learning[J].CAAI Transactions on Intelligent Systems,2024,19(4):898-908.[doi:10.11992/tis.202304010]
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Cross-modal person re-identification combining multi-scale features and confusion learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 898-908 Column: 学术论文—机器感知与模式识别 Public date: 2024-07-05
Title:
Cross-modal person re-identification combining multi-scale features and confusion learning
Author(s):
WANG Luyao1; 2; 3;  WANG Fengsui1; 2; 3;  YAN Tao1; 2; 3;  CHEN Yuanmei1; 2; 3
1. School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
2. Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China;
3. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China
Keywords:
machine vision; person re-identification; cross-modal; multi-scale characteristics; coarse-grain; fine-grain; confusion learning; modal independent attribute
CLC:
TP391.4
DOI:
10.11992/tis.202304010
Abstract:
The difficulties of cross-modal person re-identification research mainly come from the huge modal differences and intra-modal differences between pedestrian images. To address these issues, a network structure combining multi-scale features with obfuscation learning is proposed. In order to achieve high-efficiency feature extraction and reduce intra-modal differences, the network is designed as a complementary form of multi-scale features to learn local refinement features and global rough features of pedestrians respectively. The feature expression ability of the network is enhanced from fine-grained and coarse-grained aspects. Confusion learning strategy is used to fuzzy the modal identification feedback of the network, and mine the stable and effective modal-independent attributes to cope with modal differences, so as to improve the robustness of features to modal changes. In the all-search mode of the large-scale data set SYSU-MM01, the results of the first hit rate and mean average precision (mAP) of the algorithm are 76.69% and 72.45%, respectively. In the Visible to Infrared mode of the RegDB data set, the results of the first hit rate and mAP of the algorithm are 94.62% and 94.60%, respectively, which are better than the main existing methods, verifying effectiveness of the proposed method.
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