Person re-identification by two-stream feature-fusion architecture utilizing a partial body image

Yuki Hiroi, Wataru Kameyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Because of the proliferation of surveillance camera and the wide range of its utilization, 'Person Re-identification' technology has been drawing attention. However, the issues such as differences in person's appearances depending on their wearing items, clothes and behaviors still remain. Therefore, in this paper, we propose a two-stream feature-fusion architecture to improve the re-identification accuracy, where spatio-temporal features of partial body images, that we conceive to represent person's individuality robust to such differences, and the corresponding entire images, by applying convolutional LSTM and 3D CNN. The evaluation using the MARS dataset shows that the feet features are most effective among the four horizontally-split partial body images. And the CMS (Cumulative Match Score) by convolutional LSTM applied to the feet features in the proposed architecture is higher than the existing method which applies CNN and temporal pooling only to the entire images. The results show that it is effective to additionally use spatio-temporal features of feet in the MARS dataset.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages399-400
Number of pages2
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/TerritoryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • 3D CNN
  • Convolutional LSTM
  • Partial Body Image
  • Person Re-identification
  • Two-stream Feature-fusion Architecture

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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