高技术通讯2018，Vol.28Issue(3)：185-193,9.DOI:10.3772/j.issn.1002-0470.2018.03.001
基于局部最大概率特征和映射模型学习的行人再识别
Person re-identification based on local maximal occurrence feature and mapping model learning
摘要
Abstract
The technique of person re-identification is studied .Aiming to solve the problem of low re-identification rate caused by pedestrians '' own difference and the difference of camera view , a person re-identification algorithm based on local maximal occurrence features and the mapping model is proposed .Firstly , the algorithm extracts the local maximal occurrence features from pedestrians '' images, so as to overcome illumination changes and get complete image information .Secondly , it learns the cross-view mapping model to convert pedestrian features to eliminate characteristic differences of different camera views .Finally, the features will be sorted by distance metric learning . The experimental results show that the proposed person re-identification algorithm is effective enough to obtain a more complete discriminant feature representation so that it can improve the matching accuracy for person re -identi-fication.关键词
行人再识别/映射模型学习/局部最大概率特征Key words
person re-identification/mapping model learning/local maximal occurrence feature引用本文复制引用
胡正平,张敏姣,李淑芳,任大伟..基于局部最大概率特征和映射模型学习的行人再识别[J].高技术通讯,2018,28(3):185-193,9.基金项目
国家自然科学基金(61071199)和河北省自然科学基金(F2016203422)资助项目. （61071199）
