摘要
针对行人重识别中已有方法难以解决行人图像光照、视角变化大的问题,提出了一种基于特征融合的行人重识别方法。首先利用Retinex变换对图像进行预处理;然后将CN特征与原有的颜色和纹理特征融合,并通过区域和块划分的方式提取直方图获得图像特征;最后采用不同的距离学习方法在4个数据集上进行行人重识别。实验结果表明,融合后的特征对行人图像具有更好的表述能力,实现了重识别精度的较大提升,验证了方法的有效性。
Existing methods in person re-identification cannot describe the pedestrian well for significant intra-class variationsin illumination and viewpoint.In order to solve this problem,a method based on feature fusion is proposed.Firstly,the image is pre-processed using the Rentiex algorithm.Then the CN feature is combined with the color and texture features.Histograms are extracted through regional and block partition,and then the feature of image is obtained.Finally,personre-identification is performed on four public datasets with different metric learning methods.The experimental resultsshow that the feature combined with CN achieves a stronger representation and increases the accuracy obviously.
作者
张耿宁
王家宝
张亚非
徐玉龙
苗壮
ZHANG Gengning;WANG Jiabao;ZHANG Yafei;XU Yulong;MIAO Zhuang(College of Command Information System, PLA University of Science & Technology, Nanjing 210007, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第12期185-189,240,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61402519)
江苏省自然科学基金(No.BK20140071
No.BK2012512)
关键词
行人重识别
CN特征
特征融合
直方图
person re-identification
CN feature
feature fusion
histogram