贺南南,张荣国,胡静,李建伟,李晓波

• 1:太原科技大学计算机科学与技术学院

摘要(Abstract)：

针对行人重识别中信息丢失导致判别性信息缺失的问题,提出了一种多层次细粒度特征三分支网络行人重识别方法。首先,在ResNet50网络上构建中层全局特征分支、多层次全局特征分支和局部特征分支,全局分支提供更加全面的特征表示,局部特征分支提供细粒度的特征表示;其次,在三分支网络上改进了损失函数,使用权重向量和特征向量归一化以消除向量模的影响,通过构建难样本三元组损失以解决类间相似、类内差异分类问题;最后,在Market-1501和DukeMTMC-reid两个数据集上进行实验,rank-1达到了94.0%和87.4%,mAP达到了85.7%和75.5%.和现有的八种方法进行对比实验,结果表明本文方法在行人重识别中具有更好的准确率和精度。

关键词(KeyWords)： 行人重识别;多层次细粒度;特征提取;深度学习;分支网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然基金(51375132);; 山西省自然科学基金(201801D121134);; 太原科技大学博士科研启动基金(20202057)

作者(Author): 贺南南,张荣国,胡静,李建伟,李晓波

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