A Mask-Pooling Model With Local-Level Triplet Loss for Person Re-Identification

Person Re-Identification (ReID) is an important yet challenging task in computer vision. Background clutter is one of the greatest challenges to overcome. In this paper, we propose a Mask-pooling model with local-level triplet loss (MPM-LTL) to tackle this problem and improve person ReID performance...

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Auteurs principaux:	Fudan Zheng, Tingting Cai, Ying Wang, Chufu Deng, Zhiguang Chen, Huiling Zhu
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2020-01-01
Collection:	IEEE Access
Sujets:	Person re-identification mask-pooling hard triplets selection local-level triplet loss
Accès en ligne:	https://ieeexplore.ieee.org/document/9149590/
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