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People Re-identification by Graph Kernels Methods

机译：图核方法对人的重新识别

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摘要

People re-identification using single or multiple camera acquisitions constitutes a major challenge in visual surveillance analysis. The main application of this research field consists to reacquire a person of interest in different non-overlapping locations over different camera views. This paper present an original solution to this problem based on a graph description of each person. In particular, a recently proposed graph kernel is used to apply Principal Component Analysis (PCA) to the graph domain. The method has been experimentally tested on two video sequences from the PETS2009 database.
机译：使用单摄像机或多摄像机获取的人员重新识别是视觉监控分析的主要挑战。该研究领域的主要应用包括在不同的摄像机视图上的不同非重叠位置重新获得感兴趣的人。本文基于每个人的图形描述提供了针对此问题的原始解决方案。特别是，最近提出的图形内核用于将主成分分析（PCA）应用于图形域。该方法已在PETS2009数据库的两个视频序列上进行了实验测试。

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  • 来源
  • 会议地点 Munster(DE);Munster(DE)
  • 作者

    Luc Brun; Donatello Conte; Pasquale Foggia; Mario Vento;

  • 作者单位

    GREYC UMR CNRS 6072 ENSICAEN-Université de Caen Basse-Normandie,;

    Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Université di Salerno, Via Ponte Don Melillo, 1 I-84084 Fisciano (SA), Italy;

    Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Université di Salerno, Via Ponte Don Melillo, 1 I-84084 Fisciano (SA), Italy;

    Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Université di Salerno, Via Ponte Don Melillo, 1 I-84084 Fisciano (SA), Italy;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息处理（信息加工）;
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