Person Re-identification Based on Feature Location and Fusion

Abstract

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,a parallel 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.

Keywords

• person re-identification|pedestrian attribute|multi-task learning|feature location|feature fusion