Feature mapping space and sample determination for person re-identification

作     者：HOU Wei HU Zhentao LIU Xianxing SHI Changsen 侯巍;HU Zhentao;LIU Xianxing;SHI Changsen

作者机构：School of Artificial IntelligenceHenan UniversityZhengzhou 450046P.R.China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2022年第28卷第3期

页      面：237-246页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Supported by the National Natural Science Foundation of China (No.61976080) the Science and Technology Key Project of Science and Technology Department of Henan Province (No.212102310298) the Innovation and Quality Improvement Project for Graduate Education of Henan University (No.SYL20010101) the Academic Degress&Graduate Education Reform Project of Henan Province (2021SJLX195Y) 

主　　题：person re-identification(Re-ID) mapping space feature optimization sample determination 

摘      要：Person re-identification(Re-ID) is integral to intelligent monitoring ***,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person *** approach for person re-identification based on feature mapping space and sample determination is *** first,a weight fusion model,including mean and maximum value of the horizontal occurrence in local features,is introduced into the mapping space to optimize local ***,the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature ***,considering the influence of the size of samples on metric learning performance,the appropriate metric learning is selected by sample determination method to further improve the performance of person *** results on the VIPeR,PRID450 S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods.