Common visual part alignment for vehicle re‐identification

Lu, Zefeng; Lin, Ronghao; Deng, Huahui; Hu, Haifeng; Chen, Zhenwu

doi:10.1049/ell2.12457

Electronics Letters

2022

DOI: 10.1049/ell2.12457

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Common visual part alignment for vehicle re‐identification

Zefeng Lu

Ronghao Lin

Huahui Deng³

et al.

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2024

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Cited by 2 publications

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“…Introduction: With the development of deep learning, person reidentification (Re-ID) has been studied extensively [1][2][3][4][5][6][7]. Under occlusion conditions, the original images contain fewer valid features as well as more occlusion noise, which leads to the failure of instance matching.…”

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confidence: 99%

Semantically enhanced attention map‐driven occluded person re‐identification

Ge,

Yu,

Chen

et al. 2024

Electronics Letters

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Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID.

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confidence: 99%

Semantically enhanced attention map‐driven occluded person re‐identification

Ge,

Yu,

Chen

et al. 2024

Electronics Letters

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Semantic-Oriented Feature Coupling Transformer for Vehicle Re-Identification in Intelligent Transportation System

Huang

Pei

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Common visual part alignment for vehicle re‐identification

Cited by 2 publications

References 12 publications

Semantically enhanced attention map‐driven occluded person re‐identification

Semantically enhanced attention map‐driven occluded person re‐identification

Semantic-Oriented Feature Coupling Transformer for Vehicle Re-Identification in Intelligent Transportation System

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