Person reidentification via multi-feature fusion with adaptive graph learning

Runwu Zhou, Xiaojun Chang, Lei Shi, Yi-Dong Shen, Yi Yang, Feiping Nie

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of nonoverlapping surveillance cameras. Most existing works follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised Re-ID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate multi-feature dictionary learning and adaptive multi-feature graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective. Extensive experiments on four benchmark data sets demonstrate the superiority and effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1592-1601
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number5
DOIs
Publication statusPublished - May 2020

Keywords

  • Adaptive graph learning
  • feature representation learning
  • multi-feature fusion
  • person reidentification (Re-ID)

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