Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.