Task-specific neural networks for pose estimation in person re-identification task

Kai Lv, Hao Sheng, Yanwei Zheng, Zhang Xiong, Wei Li, Wei Ke

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Person re-identification is a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. To alleviate the impact of different poses, more and more studies have been done in pose estimation. In this work, we present three task-specific neural networks (TNN) algorithm designed to address the problem of pose estimation for re-identification in both single-shot and multi-shot matching. In order to recognize the human pose as one of the four classes (front, back, left, right), a PoseNet-A is first required to estimate the pose as class front-back or class left-right. Based on the results, we select the appropriate network (PoseNet-B1, PoseNet-B2) to obtain the final pose. According to the results, our method achieves very good results on a large data set (CUHK03-Pose). One thing that needs to be pointed out is that we build the dataset CUHK03-Pose which is based on the person re-identification dataset CUHK03.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Abdulmotaleb El Saddik, Xiaopeng Fan, Shuqiang Jiang, Qingming Huang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319773797
Publication statusPublished - 2018
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sept 201729 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10735 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th Pacific-Rim Conference on Multimedia, PCM 2017


  • Deep learning
  • Pose estimation
  • Re-identification
  • Single-shot


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