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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2022

15.01.2022

TriEP: Expansion-Pool TriHard Loss for Person Re-Identification

verfasst von: Zhi Yu, Wencheng Qin, Lamia Tahsin, Zhiyong Huang

Erschienen in: Neural Processing Letters | Ausgabe 3/2022

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Abstract

Person re-identification aims to identify the same person across different cameras, which is widely applied in the intelligent monitoring field. The research of TriHard loss has been verified to improve performance efficiently in the person re-identification task. However, TriHard loss considers the hardest positive sample and the hardest negative sample exclusively, which ignores the remaining samples. To stuff this gap, we propose an expansion-pool TriHard (TriEP) loss which can give attention to the hardest samples and other samples. Initially, the equal-label sample pools are expanded based on the labels of the hardest samples. Then, the statistics of the sample pool are calculated according to the distribution of samples. Finally, the dynamic penalty is imposed on TriHard loss to construct TriEP loss. Extensive experiments on Market-1501, DukeMTMC, and occluded datasets prove the superiority of the proposed TriEP loss. Compared with the baseline model, a noticeable performance improvement can be obtained after embedding the proposed TriEP loss.
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Metadaten
Titel
TriEP: Expansion-Pool TriHard Loss for Person Re-Identification
verfasst von
Zhi Yu
Wencheng Qin
Lamia Tahsin
Zhiyong Huang
Publikationsdatum
15.01.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10736-y

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