基于增强特征融合网络的行人重识别方法

针对行人重识别技术受遮挡、背景冗余、光照、姿态以及检测误差等问题的影响,鲁棒的行人特征表达对正确检索行人越来越重要.为了利用对齐特征和度量学习的优势,进一步分析局部空间语义特征.首先,在特征层面:一是在ResNet50框架中嵌入空间变换结构,自适应对齐局部区域空间特征,解决因局部区域不对齐导致的空间语义不一致的问题;二是通过对齐的局部特征设计一种增强特征融合网络,充分利用语义信息间的关联性提取图像的细节特征.然后,在损失函数层面:提出一种排序矩阵方法选取区域样本对,设计了一种局部三元组损失计算方法,联合正则化分类损失共同训练网络,充分利用融合的增强特征,达到高效度量的效果.最后,文中方法结合现有的重排算法进一步提高了Rank-1与mAP检索精度,在行人重识别基准数据集Market-1501上的实验结果,证明了本文方法的有效性.

Strong Feature Fusion Networks for Person Re-Identification

With the development of deep learning,the performance of person Re-Identification(Re-ID)has been significantly improved.It’s still a challenging task due to the challenges coming from large variations on persons such as occlusion,background clutter,pose,illumination and detection failure,etc.To retrieve true pedestrians,robust feature expression is significant.Instead of using external cues,this paper takes advantage of robust alignment features and metric learning.First,from the aspect of feature extraction,there were three contributions.(i)Embeded a spatial transformer network in the network architecture,which is called ResNet_STN in this paper,which can solve the problem of local spatial semantic feature inconsistency,accurately express the main characteristics of the target,and achieve pedestrian alignment.(ii)Designed a strong feature fusion network based on the aligned local features,which is named a Strong Feature Fusion Module(SFFM)and can make full use of the connection between semantic information to extract detailed features of images.Then,from the aspect of metric loss function,one contribution was put forward.(iii)Proposed a Ranking Matrix(RM)method to select local triplet samples and compute local triplet loss.We combined a regularized classification loss to train the network to unleash the discrimination ability of the learned strong representations of this network architecture.Finally,the proposed method with the existing re-ranking algorithm to further improves Rank-1 and mAP retrieval accuracy.Experimental results on Market-1501 dataset demonstrate the effectiveness of our proposed method.