跨摄像头场景中依赖面向标签映射关系的学习以提高识别精度，有监督行人重识别模型虽然识别精度较好，但存在可扩展问题，诸如算法识别精度严重依赖有效的监督信息，算法实时性差等；针对上述问题，提出一种基于软多标签的无监督行人重识别算法。为了提高标签匹配精度，首先利用软多标签逼近真实标签，通过计算参考数据集和参考代理在软多标签函数中的损失函数，预训练参考数据集，并构建预训练与训练结果的映射模型。再通过生成数据和真实数据分布的最小距离的期望即简化的2-Wasserstein距离计算相机视图中软多标签均值和标准差得到损失函数，解决跨视域标签一致性问题。为了提高软多标签对未标记目标数据集的有效性，计算联合嵌入损失，挖掘不同类别间的相似对，纠正跨域分布错位。针对残差网络训练时长和无监督学习精度低的问题，通过结合压缩激励网络(SENet)和多层级深度特征融合改进残差网络的结构，提高训练速度和精度。实验结果表明，该方法在标准数据集下的首位命中率和平均精度均值优于先进相关算法。
软多标签和深度特征融合的无监督行人重识别
作者单位信息
通信作者： zbh_wj2004@imust.edu.cn
出版日期：2020年12月22日
摘要
参考文献
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基金项目：
国家自然科学基金资助项目(61962046，61663036，61841204)；内蒙古杰青培育项目(2018JQ02)；内蒙古草原英才，内蒙古青年科技创新人才项目(第一层次)；内蒙古自治区自然科学基金资助项目(2015MS0604，2018MS06018)；内蒙古自治区高等学校科学技术研究项目资助(NJZY145)
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引用本文：
张宝华, 朱思雨, 吕晓琪, 等. 软多标签和深度特征融合的无监督行人重识别[J]. 光电工程, 2020, 47(12): 190636.
