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Visible Infrared Person Re-Identification via Global-Level and Local-Level Constraints
- Source :
- IEEE Access, Vol 9, Pp 166339-166350 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Visible infrared person re-identification (VI-ReID) is an extremely challenging task. VI-ReID suffers from two challenges. One is the cross-modality discrepancy due to different camera spectrums, the other is the intra-modality variation caused by the noise of background clutter or occlusion. We propose a global-level and local-level constraints network (GLoC-Net) to learn discriminative feature representations. It mainly contains two aspects. 1) We employ a non-local attention mechanism for extracting shared features to mitigate the cross-modality discrepancy, and present the division operation of local features to alleviate the problem that the non-local attention mechanism is less robust to noise. 2) We propose joint constraints of global-level and local-level to alleviate the intra-modality variation, which makes the algorithm more robust to noise. Experiments demonstrate that the superior performance of proposed method compared with the state-of-the-arts.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....56fdf39631920446e6c74526ef8f8e50