Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (11): 3314-3319.DOI: 10.11772/j.issn.1001-9081.2020030351

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Person re-identification algorithm based on low-pass filter model

HUA Chao1, WANG Gengrun1, CHEN Lei2   

  1. 1. Information Engineering University, Zhengzhou Henan 450001, China;
    2. PLA 31101 Troop
  • Received:2020-03-25 Revised:2020-06-19 Online:2020-11-10 Published:2020-07-22
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61801515).

基于低通滤波模型的行人再识别算法

花超1, 王庚润1, 陈雷2   

  1. 1. 信息工程大学, 郑州 450001;
    2. 解放军 31101部队
  • 通讯作者: 王庚润(1987-),男,安徽蒙城人,助理研究员,博士,主要研究方向:电信网安全、数据处理;wanggengrun@gmail.com
  • 作者简介:花超(1989-),男,安徽郎溪人,讲师,硕士,主要研究方向:网络与信息安全;陈雷(1992-),男,江苏淮安人,研究实习员,硕士,主要研究方向:图像处理
  • 基金资助:
    国家自然科学基金资助项目(61801515)。

Abstract: Because a large number of useless features exist in the image of person re-identification due to occlusion and background interference, a person re-identification method based on low-pass filtering model was proposed. First, the person images were divided into blocks. Then the similar number of small blocks in each image were calculated. Among them, the blocks with higher similarity number were marked as high-frequency noise features and the blocks with smaller similarity number were the beneficial features. Finally, different from the low-pass filter which filtered the mutation features and maintained the smooth features in the common image processing, the low-pass filter in the communication system was used to achieve the goal of suppressing high-frequency noise features and gain beneficial features in the proposed method. Experimental results show that the identification rate of the proposed method on ETHZ dataset is nearly 20% higher than that of the classic Symmetry-Driven Accumulation of Local Features (SDALF) method, and at the same time, this method achieves similar results on VIPeR (Viewpoint Invariant Pedestrian Recognition) and I-LIDS (Imagery Library for Intelligent Detection Systems) datasets.

Key words: person re-identification, low-pass filter, high-frequency noise, similarity, noise frequency

摘要: 针对行人再识别的图像中由于遮挡和背景干扰而存在大量无用特征的问题,提出一种基于低通滤波模型的行人再识别方法。首先,将行人图像进行分块;然后,计算各种小块在各图像中的相似个数,其中相似个数较多的小块为高频噪声特征、相似个数较少的小块为有益特征;最后,不同于常见图像处理中的滤除突变特征、留下平滑特征的低通滤波器,所提方法利用通信系统中的低通滤波器实现抑制高频噪声特征、增益有益特征的目标。实验结果表明,所提方法在ETHZ数据集上的识别率比经典的对称性局部特征累加(SDALF)方法提升了近20%;同时,该方法在VIPeR和I-LIDS数据集上也取得了相似的效果。

关键词: 行人再识别, 低通滤波, 高频噪声, 相似度, 噪声频率

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