基于深层特征融合的行人重识别方法

Pedestrian re-identification based on deep feature fusion

针对现有基于深度学习的行人重识别方法对于行人姿态变化、部分遮挡等引起的行人判别特征信息缺失的问题,提出了一种深层特征融合的行人重识别方法。首先,利用卷积层和池化层多次提取网络深层特征,从空间维度提升网络性能,使用融合后的深层特征作为行人图像的全局特征属性;其次,为提高模型的泛化能力,在深层融合特征后加入一个批量归一化层,同时采用标签平滑损失函数和三元组损失函数对模型进行联合训练。实验结果表明,所提的深层特征融合方法具有很好的表达能力。在Market1501、DukeMTMC-reID、CUHK03和MSMT174个数据集上对所提方法进行了验证,其中在Market1501数据集上,Rank-1值达到了95.0%,mAP达到了85.6%。

Aiming at the problem that the existing deep learning based pedestrian re-identification methods lack pedestrian discriminative feature information caused by pedestrian posture change and partial occlusion,a pedestrian re-identification network model based on deep feature fusion is proposed.Firstly,the convolution layer and the pooling layer are used to extract the deep features of the network many times to improve the network performance from the spatial dimension.Secondly,in order to improve the generalization ability of the model,a batch normalization layer is added after the deep fusion features.Finally,the label smoothing loss function and the triple loss function are used to train the model.The experimental results show that the proposed deep feature fusion method has good expression ability.The proposed method is validated on four datasets:Market1501,Duke MTMC-reID,CUHK03 and MSMT17.On Market1501,Rank-1 and mAP reach 95.0%and 85.6%.