基于特征定位与融合的行人重识别算法

Person Re-identification Based on Feature Location and Fusion

行人外观属性是区分行人差异的重要语义信息。行人属性识别在智能视频监控中有着至关重要的作用,可以帮助我们对目标行人进行快速的筛选和检索。在行人重识别任务中,可以利用属性信息得到精细的特征表达,从而提升行人重识别的效果。文中尝试将行人属性识别与行人重识别相结合,寻找一种提高行人重识别性能的方法,进而提出了一种基于特征定位与融合的行人重识别框架。首先,利用多任务学习的方法将行人重识别与属性识别结合,通过修改卷积步长和使用双池化来提升网络模型的性能。其次,为了提高属性特征的表达能力,设计了基于注意力机制的平行空间通道注意力模块,它不仅可以在特征图上定位属性的空间位置,而且还可以有效地挖掘与属性关联度较高的通道特征,同时采用多组平行分支结构减小误差,进一步提高网络模型的性能。最后,利用卷积神经网络设计特征融合模块,将属性特征与行人身份特征进行有效融合,以获得更具鲁棒性和表达力的行人特征。实验在两个常用的行人重识别数据集DukeMTMC-reID和Market-1501上进行,结果表明,所提方法在现有的行人重识别方法中处于领先水平。

Pedestrian appearance attributes are important semantic information distinguishing pedestrian differences.Pedestrian attribute recognition plays a vital role in intelligent video surveillance,which can help us quickly screen and retrieve target pedestrians.In the task of person re-identification,we can use attribute information to obtain fine feature expressions,thereby improving the effect of pedestrian re-identification.This paper attempts to combine pedestrian attribute recognition with person re-identification,looking for a way to improve the performance of person re-identification,and proposes a person re-identification framework based on feature positioning and fusion.Firstly,we use the method of multi-task learning to combine person re-identification with attribute recognition,and improve the performance of the network model by modifying the convolution step size and using double pooling.Secondly,to improve the expression ability of attribute features,aparallel spatial channel attention module based on the attention mechanism is designed.It can not only locate the spatial position of the attribute on the feature map,but also can effectively mine the channel with higher correlation with the attribute features,and uses multiple groups of parallel branch structure to reduce errors and further improve the performance of the network model.Finally,we use the convolutional neural network to design the feature fusion module to effectively integrate the attribute features and pedestrian identity features to obtain more robust and expressive pedestrian features.The experiment is conducted on two commonly used person re-identification datasets DukeMTMC-reID and Market-1501.The results show that this method is at the leading level among the existing person re-identification methods.