Pedestrian Reidentification Based on Feature Fusion and Metric Learning

Abstract

The pedestrian recognition in public environment is influenced by the pedestrian environment and the dynamic characteristic boundary factors, so it is easy to produce the tracking error. In order to improve the ability of pedestrian re-identification in public environment, we need to carry out feature fusion and metric learning, and propose pedestrian re-identification based on feature fusion and metric learning. The geometric grid area model of pedestrian recognition in public environment is constructed, the method of fuzzy dynamic feature segmentation is used to reconstruct the dynamic boundary feature point of pedestrian recognition in public environment, the method of bottom-up modeling is used to design the dynamic area grid model of pedestrian recognition in public environment, the design of dynamic area grid model is three-dimensional grid area, the grayscale pixel set of pedestrian recognition dynamic constraint under public environment is extracted, the boundary feature fusion is carried out according to the distribution intensity of grayscale, the image fusion and enhancement information processing of pedestrian recognition under public environment, and the method of 3D dynamic constraint is used to realize the local motion planning of pedestrian recognition under public environment, and the recognition feature fusion and learning of pedestrian recognition under public environment is realized according to the result of contour segmentation. The simulation results show that the method is used for pedestrian recognition again in public environment, and the fuzzy judgment ability of pedestrian dynamic edge features is strong, which makes the error controlled below 10 mm, and the fluctuation of pedestrian recognition again is more stable, the recognition accuracy is higher and the robustness is better.