信息与控制2017，Vol.46Issue(5)：525-529,542,6.DOI:10.13976/j.cnki.xk.2017.0525
基于核学习和距离相似度量的行人再识别
Person Re-identification using Kernel Learning and Similarity-distance Metric
摘要
Abstract
Person re-identification involves matching pedestrian images observed from different cameras in non-over-lapping multi-camera surveillance systems. In this article, a person re-identification method based on kernel-based similarity metric learning is proposed. First, the dimension of person fusion features is reduced by PCA whitening. Second, the kernel trick is used to deal with reduced features. Finally, the similarity metric func-tion, which combines similarity and distance functions, is applied to the system;it helps the system learn af-fine transformation, which shows pairwise contrast. The result based on VIPeR, iLIDS, ETHZ, and CUHK01 datasets shows a significant improvement in performance measured in cumulative match characteristic curves. The proposed method is robust to different viewpoints, illumination changes, varying poses, and the effects of occlusion.关键词
行人再识别/测度学习/核学习Key words
person re-identification/metric learning/kernel learning分类
信息技术与安全科学引用本文复制引用
胡彬,邵叶秦..基于核学习和距离相似度量的行人再识别[J].信息与控制,2017,46(5):525-529,542,6.基金项目
国家自然科学基金青年基金资助项目(61305134) （61305134）
江苏省教育厅自然科学基金资助项目(16KJB520037) （16KJB520037）
南通市前沿与关键技术创新项目(MS22015100) （MS22015100）
南通市应用研究项目(GY12015031) （GY12015031）
