Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology. Issue 3 (4th March 2023)

• Record Type: Journal Article
• Authors: Chang, Heyu; Qu, Dan; Wang, Kun; Zhang, Hongqi; Si, Nianwen; Yan, Gengxiao; Li, Huazhong
• Is Part Of: Enterprise information systems. Volume 17:Issue 3(2023)
• Publication Date: 2023-03-04
• Subjects: Person re-identification -- attention mechanism -- attribute recognition -- intelligent surveillance -- big data analysis -- cloud computing; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2021.1941274
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance.