Abstract:To address the occlusion problem in person re-identification, this study presents a person re-identification method based on pose-driven local feature alignment. The network mainly consists of a pose encoder (PE) and a human part alignment module (HPAM). Specifically, the PE restrains the confidence of the key points on the bones in obscured areas by reconstructing the pose estimation heatmap to guide the network to extract the features of the person’s visible parts. The HPAM extracts the person’s local features according to the confidence map of the key points output by the PE for feature alignment, which further reduces the interference of non-person features. The simulation and experiments on occlusion datasets and half-body datasets show that the proposed method delivers better results than those produced by other networks under comparison.