Re-Identification for Multi-Object Tracking Using Triplet Loss

Koung Suk Ko, Woo Jin Ahn, Geon Hee Kim, Myo Taeg Lim, Tae Koo Kang, Dong Sung Pae

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

Abstract

Assigning a consistent identification(ID) number is a chronic problem in the tracking model. However, recent tracking models lose the ID because it focuses only on the previous frame. This paper constructed a tracking deep learning model using triplet loss to give consistent ID to objects detected while tracking. We also show the best way for pre-processing the input for the triplet-tracking model, which inputs various image sizes. The experimental result of 97.76% accuracy on KITTI shows the effectiveness of our result.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages525-527
Number of pages3
ISBN (Electronic)9781728191003
DOIs
Publication statusPublished - 2021 Jan 13
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 132021 Jan 16

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
CountryKorea, Republic of
CityJeju Island
Period21/1/1321/1/16

Keywords

  • Metric Learning
  • Multi-Object Tracking
  • Re-Identification
  • Triplet Loss

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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