Video-Based Person Re-identification by 3D Convolutional Neural Networks and Improved Parameter Learning

Naoki Kato, Kohei Hakozaki, Masamoto Tanabiki, Junko Furuyama, Yuji Sato, Yoshimitsu Aoki

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

1 Citation (Scopus)

Abstract

In this paper we propose a novel approach for video-based person re-identification that exploits convolutional neural networks to learn the similarity of persons observed from video camera. We take 3-dimensional convolutional neural networks (3D CNN) to extract fine-grained spatiotemporal features from the video sequence of a person. Unlike recurrent neural networks, 3D CNN preserves the spatial patterns of the input, which works well on re-identification problem. The network maps each video sequence of a person to a Euclidean space where distances between feature embeddings directly correspond to measures of person similarity. By our improved parameter learning method called entire triplet loss, all possible triplets in the mini-batch are taken into account to update network parameters. This parameter updating method significantly improves training, enabling the embeddings to be more discriminative. Experimental results show that our model achieves new state of the art identification rate on iLIDS-VID dataset and PRID-2011 dataset with 82.0%, 83.3% at rank 1, respectively.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
PublisherSpringer Verlag
Pages153-163
Number of pages11
ISBN (Print)9783319929996
DOIs
Publication statusPublished - 2018 Jan 1
Event15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal
Duration: 2018 Jun 272018 Jun 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10882 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Image Analysis and Recognition, ICIAR 2018
CountryPortugal
CityPovoa de Varzim
Period18/6/2718/6/29

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kato, N., Hakozaki, K., Tanabiki, M., Furuyama, J., Sato, Y., & Aoki, Y. (2018). Video-Based Person Re-identification by 3D Convolutional Neural Networks and Improved Parameter Learning. In Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings (pp. 153-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10882 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93000-8_18