基于多粒度特征融合网络的行人重识别

Multi-granularity Feature Fusion Network for Person Re-Identification

行人重识别旨在跨监控设备下检索出特定的行人目标.为捕捉行人图像的多粒度特征进而提高识别精度,基于OSNet基准网络提出一种多粒度特征融合网络(Multi-granularity Feature Fusion Network for Person Re-Identification,MFN)进行端对端的学习.MFN由全局分支、特征擦除分支和局部分支组成,其中特征擦除分支由双通道注意力擦除模型构成,此模型包含通道注意力擦除模块(Channel Attention-based Dropout Moudle,CDM)和空间注意力擦除模块(Spatial Attention-based Dropout Moudle,SDM).CDM对通道的注意力强度排序并擦除低注意力通道,SDM在空间维度上以一定概率擦除最具有判别力的特征,两者通过并联方式相互作用,提高模型的识别能力.全局分支采用特征金字塔结构提取多尺度特征,局部分支将特征均匀切块后级联成一个单一特征,提取关键局部信息.大量实验结果表明了本文方法的有效性,在Market1501、DukeMTMC-reID和CUHK03-Labeled(Detected)数据集上,mAP/Rank-1分别达到了90.1%/95.8%、81.8%/91.4%和80.7%/82.3%(78.7%/81.6%),大幅优于其他现有方法.

For the purpose of capturing the multi-granularity features and improving the recognition accuracy,a multi-granularity feature fusion network for person re-identification(MFN)is proposed based on the omist-scale network(OSNet).The MFN network is composed of a global branch,a feature dropout branch and a local branch.The feature dropout branch consists of a dual-channel attention dropout model,which includes a channel attention-based dropout moudle(CDM)and a Spatial attention-based dropout moudle(SDM).CDM sorts the attention intensity and dropouts low attention channels,and SDM dropouts the most discriminative features with a certain probability in the spatial dimension.The global branch uses the feature pyramid structure to extract multi-scale features,and the local branch employs a uniform partition strategy to produce local features which are cascaded into a single one for key local information extraction.Experiments on the large scale datasets show the effectiveness of MFN.On the Market1501,DukeMTMC-reID and CUHK03-Labeled(Detected)datasets,mAP/Rank-1 of MFN reaches 90.1%/95.8%,81.8%/91.4%and 80.7%/82.3%(78.7%/81.6%),which is superior to other existing methods.