【摘要】 针对卷积神经网络显式学习空间不变性的能力较弱以及行人重识别任务中存在遮挡、背景干扰等导致辨别性特征概率性丢失的问题,提出了一种结合空间特征学习与多粒度特征融合的行人重识别方法。首先,提出注意力空间转换网络(A-STN),用于学习空间特征,解决行人空间特征未对齐的问题;然后,将网络分为全局分支、局部粗粒度融合分支和局部细粒度融合分支,分别提取行人全局特征、粗粒度融合特征和细粒度融合特征。其中全局分支通过融合不同池化特征丰富全局特征;局部粗粒度融合分支利用重叠池化增强每个局部特征的同时学习多粒度特征之间的关联关系;局部细粒度融合分支则通过利用差异化池化,得到的差异特征,与全局特征进行相互融合,以学习行人局部特征和全局特征之间的关联关系。所提出的方法在Market1501、DukeMTMC-ReID和CUHK03三个公开数据集上开展对比实验,实验结果均优于所对比的方法,验证了所提方法的有效性。更多还原

【Abstract】 In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks,a person re-identification method combining spatial feature learning and multi-granularity feature fusion was proposed.First,an attention spatial transformation network(A-STN) is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial features.Then the network was divided into a global branch,a local coarse-grained fusion branch,and a local fine-grained fusion branch to extract pedestrian global features,coarse-grained fusion features,and fine-grained fusion features,respectively.Among them,the global branch enriches the global features by fusing different pooling features.The local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity features.The local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global features.Finally,the proposed method was compared on three public datasets:Market1501,DukeMTMC-ReID and CUHK03.The experimental results were better than those of the comparative methods,which verifies the effectiveness of the proposed method.更多还原