多置信度重排序的行人再识别算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201711004

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Abstract To improve the low recognition rate caused by the poor similarity measure in person re-identification, a method of person re-identification via multiple confidences re-ranking is proposed, and the accuracy of person re-identification is improved by evaluating the confidences of test samples. Firstly, the target samples and test samples are described by characteristics from deep learning network ResNet50, and the initial ranking is obtained based on the similarity between the target samples and test samples. Secondly, the classified sample sets are formed by samples with similar ranking, and then the cluster center of each category, the minimum, maximum and mean distance between samples and cluster center are acquired to set three confidence intervals with different confidences. Finally, Jaccard distance is used to sort the similarity between the target samples and test samples. The experimental results of three standard test datasets verify the effectiveness of the proposed algorithm.

Key words： Person Re-identification Multiple Confidence Interval Re-ranking Jaccard Distance

Received: 28 June 2017

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No.61502385)

About author:: 李姣,女,1991年生,硕士研究生,主要研究方向为智能视频监控、模式识别、数字图像处理.E-mail:18710385741@163.com.
张晓晖,男,1973年生,博士,教授,主要研究方向为图像处理与信息融合、智能机器人技术、基于网络的控制系统.E-mail: xhzhang@xaut.edu.cn.
朱虹(通讯作者),女,1963年生,博士,教授,主要研究方向为数字图像处理、智能视频监控、模式识别.E-mail:zhuhong@xaut.edu.cn.
王婧,女,1985年生,博士研究生,主要研究方向为数字图像处理、智能视频监控、模式识别.E-mail:jjing63@hotmail.com.

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	LI Jiao
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Cite this article:

LI Jiao,ZHANG Xiaohui,ZHU Hong等. Person Re-identification via Multiple Confidences Re-ranking[J]. , 2017, 30(11): 995-1002.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201711004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I11/995

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