Segment attention‐guided part‐aligned network for person re‐identification. Issue 13 (16th April 2021)

• Record Type: Journal Article
• Authors: Wang, Wen; Liu, Yongwen; An, Gaoyun
• Is Part Of: Electronics letters. Volume 57:Issue 13(2021)
• Page Start: 508
• Page End: 510
• Publication Date: 2021-04-16
• Subjects: Electronics -- Periodicals; 621.381
• DOI: 10.1049/ell2.12178
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract: Part misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re‐identification. This letter examines Res2Net as the backbone network to extract multi‐scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re‐calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG‐PAN effectively integrates the global appearance features of person image with part fine‐grained features. The experimental results on the Market‐1501, DukeMTMC‐reID and CUHK03 datasets show that the proposed SAG‐PAN achieved superior performance against the existing state‐of‐the‐art methods.