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People Re-identification by Graph Kernels Methods

机译:通过图形内核方法重新识别

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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:人们使用单一或多个摄像机采集重新识别构成视觉监控分析中的主要挑战。本研究领域的主要应用包括在不同的相机视图中重新判断不同的非重叠位置的感兴趣者。本文基于每个人的图表描述为此问题提出了原始解决方案。特别地,最近建议的图形内核用于将主成分分析(PCA)应用于图形域。该方法已经在PETS2009数据库的两个视频序列上进行了实验测试。

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