计算机技术与发展2024，Vol.34Issue(4)：89-94,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0014
基于姿态引导特征增强的遮挡行人重识别
Occluded Person Re-identification Based on Pose-guided Feature Enhancement
摘要
Abstract
To address the problems of feature loss during feature extraction and noise interference during feature matching in occluded person re-identification,a pose-guided feature enhancement model is proposed.Firstly,with the assistance of key point information,a symmetrical region feature repair module is proposed to replace the locally lost features in the occluded region with the features in the non-occluded region.Secondly,to explore the semantic relationship between local features,an adjacent region feature compensation module is proposed to update the feature representation of each local feature by combining the features of adjacent regions.Finally,by leveraging the generalized mean pooling to extract features from the central region of the feature map,the expression ability of person feature vectors is improved to obtain more accurate global features.Simulation experiments show that the proposed model outperforms most algorithms in common holistic datasets,partial datasets,and occluded datasets in terms of Rank-1 and mAP,achieving 56.7％and 72.4％Rank-1 on Occluded-Duke and Occluded-REID datasets,respectively.关键词
行人重识别/遮挡/特征修复/特征补偿/广义均值池化Key words
person re-identification/occlusion/feature repair/feature compensation/generalized mean pooling分类
信息技术与安全科学引用本文复制引用
刘志刚,王淼,刘苗苗..基于姿态引导特征增强的遮挡行人重识别[J].计算机技术与发展,2024,34(4):89-94,6.基金项目
国家自然科学基金(42002138) （42002138）
黑龙江省自然科学基金(LH2020F003,LH2021F004) （LH2020F003,LH2021F004）
黑龙江省高等教育教学改革项目(SJGY20210109) （SJGY20210109）
