A Novel Semi-Supervised Learning Approach to Pedestrian Reidentification

Hua Han, Wenjin Ma, Meng Chu Zhou, Qiang Guo, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


One of the important Internet-of-Things applications is to use image and video to realize automatic people monitoring, surveillance, tracking, and reidentification (Re-ID). Despite some recent advances, pedestrian Re-ID remains a challenging task. Existing algorithms based on fully supervised learning for it usually requires numerous labeled image and video data, while often ignoring the problem of data imbalance. This work proposes a method based on unlabeled samples generated by cycle generative adversarial networks. For a newly generated unlabeled sample, it learns its pseudorelationship between unlabeled samples and labeled ones in a low-dimensional space by using a self-paced learning approach. Then, these unlabeled ones having pseudo-relationship with labeled ones are added in a training set to better mine discriminative information between positive and negative samples, which is in turn used to learn a more effective metric. We name this method as a semi-supervised learning approach based on the built pseudopairwise relations between labeled data and unlabeled one. It can greatly enhance the performance of pedestrian Re-ID in case of insufficient labeled images. By using only about 10% labeled images in a given database, the proposed method obtains higher accuracy than state-of-the-art supervised learning methods using all labeled ones, e.g., deep-learning ones, thus greatly advancing the field of pedestrian Re-ID.

Original languageEnglish (US)
Article number9197649
Pages (from-to)3042-3052
Number of pages11
JournalIEEE Internet of Things Journal
Issue number4
StatePublished - Feb 15 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Generative adversarial networks (GANs)
  • machine learning
  • pedestrian reidentification (Re-ID)
  • pseudo-pairwise relations
  • semi-supervised learning (SSL)


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