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

  • Naoki KatoEmail author
  • Kohei Hakozaki
  • Masamoto Tanabiki
  • Junko Furuyama
  • Yuji Sato
  • Yoshimitsu Aoki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Naoki Kato
    • 1
    Email author
  • Kohei Hakozaki
    • 1
  • Masamoto Tanabiki
    • 2
  • Junko Furuyama
    • 2
  • Yuji Sato
    • 2
  • Yoshimitsu Aoki
    • 1
  1. 1.Keio UniversityTokyoJapan
  2. 2.Panasonic CorporationOsakaJapan

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