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

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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Bibliographic Details
Main Authors: Fudan Zheng, Tingting Cai, Ying Wang, Chufu Deng, Zhiguang Chen, Huiling Zhu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Person re-identification
mask-pooling
hard triplets selection
local-level triplet loss
Online Access:https://ieeexplore.ieee.org/document/9149590/

Internet

https://ieeexplore.ieee.org/document/9149590/

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