Abstract
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.
Highlights
Pedestrian re-identification is a technology to determine whether there is a specific pedestrian in an image or video sequence
When using the joint point regional structural relationship features alone, the accuracy of Rank-1 is only 72.3%. It is because the regional features of the joint points only consider the drawbacks brought about by the regional features related to movement changes, and a large amount of other spatial information is ignored
The accuracy of Rank-1 of the method in this paper increases 10.5%, which further validates the importance of the relationship model between regions
Summary
Pedestrian re-identification is a technology to determine whether there is a specific pedestrian in an image or video sequence. Given an image or video sequence of a specific pedestrian, perform pedestrian image matching in the case of cross-monitoring equipment. At present, it is widely used in intelligent video surveillance, intelligent security and other fields. It is widely used in intelligent video surveillance, intelligent security and other fields It has great application value for finding missing persons, tracing criminals, security, and monitoring. In recent years, it has attracted widespread attention in the field of computer vision. The methods to solve this problem are roughly divided into three categories, feature representation [1], metric learning [2], [3], and deep learning [4]
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