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How to extract discriminative features from redundant video information is a key issue for video pedestrian re-identification. Factors such as occlusion, perspective, and posture changes in complex environments pose severe challenges to pedestrian re-identification based on local methods. In this paper, a posture-guided multi-scale structural relationship learning pedestrian re-identification method is proposed. The purpose is to analyze the video sequence of pedestrians based on the reference pose and the pose alignment model, and extract the sample frame with the highest image quality and the most complete spatial information in the reference pose. The method based on posture guidance can more accurately eliminate the interference of background, occlusion and perspective factors. To further explore the potential relationship between local regions, this paper calculates the relationship matrix between the local regions based on the relationship model to further calculate the relationship weight, and the graph convolutional network based on the relationship weight learns the structural relationship feature of multi-scale regions. The input of the graph convolutional network is a local region divided by a multi-scale method, and the output is a pose-guided multi-scale structural relationship feature. The experimental results on three public datasets show that the proposed method performs favorably against state-of-the-art methods.

Discriminative feature learning is critical for pedestrian re-identification. Previous part-based methods mainly focus on the region of specific predefined semantics to learn local representations, ignoring the influence of posture changes, and the learning efficiency and robustness in complex scenes are poor. In this paper, a hypergraph video pedestrian re-identification method based on posture structure relationships and action constraint(PA-HVPReid) is proposed, which aims to make full use of pedestrian walking postures to obtain more discriminative features. The pose structure relationship feature solves the problem that the pooling operation destroys the feature structure relationship. This paper uses a graph convolution network (GCN) to preserve the structure relationship presented by the image feature map. The input of the GCN is the region at the joint points of the pedestrian to be detected, and the output is the color feature that retains the structural relationship. The structural relationship hypergraph is formed according to the structural relationship between the joint point regions. The action hypergraph can be constructed by constraining the action information between the joint point regions. The saliency score of the joint point region is calculated from the posture structure hypergraph and the action hypergraph. We convert the saliency score into a probability distribution problem and propose a relative entropy loss function based on regional saliency to measure the similarity of the two probability distributions. Experimental results show that the performance of our method is better than the existing method on three data sets.