Analysis of Different Methods in Pedestrian Re-identification

This study focuses on the field of pedestrian re-identification (ReID), aiming to enhance the accuracy and efficiency of individual recognition through advanced deep learning models. The article first introduces two deep learning models: Omni-Scale Feature Learning for Person Re-Identification (OSNet) and Multi-Scale Interaction Network (MSINet). OSNet employs Depthwise Separable Convolutions (DSC) and Omni-Scale Residual Blocks to improve the ability to learn features across different scales. MSINet, on the other hand, utilizes neural architecture search technology to design a lightweight network architecture, enhancing feature discrimination and flexible utilization through Twins Contrastive Mechanism (TCM) and Multi-Scale Interaction (MSI). Additionally, a Spatial Alignment Module (SAM) is proposed to enhance the consistency of images under different viewpoints or conditions. The experimental section selects two widely used pedestrian re-identification datasets, Market1501 and MSMT17, for evaluation, and the results show that MSINet outperforms existing methods in terms of accuracy and stability. The article concludes by summarizing the advantages of OSNet and MSINet in multi-scale feature learning and points out their application limitations.