Attribute-restricted latent topic model for person re-identification

Xiao Liu, Mingli Song, Qi Zhao, Dacheng Tao, Chun Chen, Jiajun Bu

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Attribute-Restricted Latent Topic Model (ARLTM) to encode targets into semantic topics. Compared to conventional topic models such as LDA and pLSI, ARLTM performs best by imposing semantic restrictions onto the generation of human specific attributes. We use MCMC EM for model learning. Experimental results show that our method achieves state-of-the-art performance.

Original languageEnglish (US)
Pages (from-to)4204-4213
Number of pages10
JournalPattern Recognition
Volume45
Issue number12
DOIs
StatePublished - Dec 2012

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China (61170142), National Key Technology R&D Program (2011BAG05B04), the Zhejiang Province Key S&T Innovation Group Project (2009R50009), and the Fundamental Research Funds for the Central Universities (2012FZA5017).

Keywords

  • Attribute-restricted latent topic model
  • Person re-identification
  • Semantic topic
  • Visual attribute

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