Open AccessPerson Re-Identification by Pose Invariant Deep Metric Learning With Improved Triplet Loss
Author(s) -
Min Chen,
Yongxin Ge,
Xin Feng,
Chuanyun Xu,
Dan Yang
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2879490
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Person re-identification (re-ID) is a challenging problem in the community which aims at identifying person in a surveillance video. Despite recent advance in the field of computer vision, person re-ID still presents great challenge since person’s presence is various under different illumination, viewpoints, occlusion, and background clutter. In this paper, to exploit more discriminative information of person’s appearance, we propose a novel pose invariant deep metric learning (PIDML) method under an improved triplet loss for person re-ID. Our approach contributes the misalignment problem and distance metric simultaneously, which are two key problems for person re-ID. Extensive experiments show that our proposed method could achieve favorable accuracy while compared with the state-of-the-art techniques on the challenging Market-1501, CUHK03, and VIPeR datasets.
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