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Fine-grained-based multi-feature fusion for occluded person re-identification.
- Source :
-
Journal of Visual Communication & Image Representation . Aug2022, Vol. 87, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- Many previous occluded person re-identification(re-ID) methods try to use additional clues (pose estimation or semantic parsing models) to focus on non-occluded regions. However, these methods extremely rely on the performance of additional clues and often capture pedestrian features by designing complex modules. In this work, we propose a simple Fine-Grained Multi-Feature Fusion Network (FGMFN) to extract discriminative features, which is a dual-branch structure consisting of global feature branch and partial feature branch. Firstly, we utilize a chunking strategy to extract multi-granularity features to make the pedestrian information contained in it more comprehensive. Secondly, a spatial transformer network is introduced to localize the pedestrian's upper body, and then introduce a relation-aware attention module to explore the fine-grained information. Finally, we fuse the features obtained from the two branches to obtain a more robust pedestrian representation. Extensive experiments verify the effectiveness of our method under the occlusion scenario. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 87
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 158482409
- Full Text :
- https://doi.org/10.1016/j.jvcir.2022.103581