Person Reidentification by Joint Local Distance Metric and Feature Transformation

Zimo Liu, Huchuan Lu, Xiang Ruan, Ming Hsuan Yang

Research output: Contribution to journalArticle

2 Citations (Scopus)


Person reidentification is of great importance in visual surveillance and multiperson tracking across multiple camera views. Two fundamental problems are critical for person reidentification: 1) how to account for appearance variation or feature transformation caused by viewpoint changes and 2) how to learn a discriminative distance metric for reidentification. In this paper, we propose an algorithm in which both feature transformation and metric learning are exploited and jointly optimized. We learn local models from subsets of training samples with regularization imposed by the global model which is trained among the entire data set. The learned local models enhance the discriminative strength and generalization ability. Experimental results on the Viewpoint Invariant PEdestrian Eecognition, Queen Mary University of London ground reidentification, CUHK01, and CUHK03 benchmark data sets show that the proposed sample-specific view-invariant approach performs favorably against the state-of-The-Art person reidentification methods.

Original languageEnglish
Article number8666132
Pages (from-to)2999-3009
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Publication statusPublished - 2019 Oct

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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