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Multichannel-Kernel Canonical Correlation Analysis for Cross-View Person Reidentification

Published:06 March 2017Publication History
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Abstract

In this article, we introduce a method to overcome one of the main challenges of person reidentification in multicamera networks, namely cross-view appearance changes. The proposed solution addresses the extreme variability of person appearance in different camera views by exploiting multiple feature representations. For each feature, kernel canonical correlation analysis with different kernels is employed to learn several projection spaces in which the appearance correlation between samples of the same person observed from different cameras is maximized. An iterative logistic regression is finally used to select and weight the contributions of each projection and perform the matching between the two views. Experimental evaluation shows that the proposed solution obtains comparable performance on the VIPeR and PRID 450s datasets and improves on the PRID and CUHK01 datasets with respect to the state of the art.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 13, Issue 2
      May 2017
      226 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3058792
      Issue’s Table of Contents

      Copyright © 2017 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 March 2017
      • Revised: 1 December 2016
      • Accepted: 1 December 2016
      • Received: 1 September 2016
      Published in tomm Volume 13, Issue 2

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