Abstract: Extracting both global and local features from pedestrian images has become the mainstream inperson re-identification.While among most of current deep learning based person re-identification models,the relations between adjacent body parts are seldom taken into consideration during extracting local features.This may decay the capability of distinguishing different persons when they share similar attributes of local regions.To address this problem,a novel method is proposed to learn region correlated deep features for person re-identification.In our model,the output feature map of backbone network is partitioned with multiple granularities first.And then the structure information preserved local features are learned via a new designed Region Correlated Network (RCNet) module.The RCNet makes full use of the structure maintenance of average pooling and the performance advantage of max pooling,endowing local features with rich structural information.By jointly processing current feature and local features from other regions,they are strongly related to each other due to the spatial correlation.As a result,the discrimination of them is significantly enhanced.For better optimization of the whole network,the shortcut connection in deep residual networks is also employed in the architecture of RCNet.Finally,the re-identification is conducted with both global features and the local features with structural information incorporated.Experimental results show that the proposed method achieves higher matching accuracies in comparison with existing approaches on the public Market-1501,CUHK03 and DukeMTMC-reID datasets,demonstrating favorable re-identification performance.

Key words: Deep Learning, Feature representation, Person re-identification, Pooling operation, Region correlated network