Pedestrian re-identification method based on multi-scale feature cutting and fusion

Abstract

The invention provides a pedestrian re-identification method based on multi-scale feature cutting and fusion, particularly provides pedestrian re-identification network training based on multi-scale depth feature cutting and fusion and a pedestrian re-identification method based on the network, and performs pedestrian re-identification through multi-scale global descriptor extraction and local descriptor extraction. The extraction of the global descriptor is to carry out average pooling and feature fusion on feature maps of different layers of the deep network, and the extraction of the localdescriptor is to horizontally divide the feature map of the deepest layer of the deep network into a plurality of blocks and respectively extract the local descriptors corresponding to the feature maps. In the training process, a minimum smooth cross entropy cost function and a difficult sample sampling triple cost function are used as the target training network parameters. By adopting the technical scheme of the invention, the problem of feature mismatching caused by factors such as pedestrian posture change and camera color cast in pedestrian re-identification can be solved, and the influence caused by background can be eliminated, so that the robustness and precision of pedestrian re-identification are improved.

Description

technical field [0001] The invention relates to the technical fields of computer vision and image processing, in particular to a pedestrian re-identification method based on multi-scale feature cutting and fusion. Background technique [0002] Pedestrian re-identification is a technology that matches whether two objects under different viewing angles of non-overlapping cameras are the same target, especially in security and criminal aspects, which has received extensive attention and applications. However, there are still huge challenges in pedestrian re-identification technology. Due to the fact that it is easily affected by factors such as illumination, viewing angle, and background in actual situations, the intra-class (same pedestrian) difference between pedestrians is even greater than that of inter-class (different pedestrians). discrepancy, leading to the failure of the re-identification task. In the actual pedestrian re-identification research work, it is mainly div...

AI Technical Summary

Problems solved by technology

[0003] Most current methods ignore the influence of irrelevant background information in the process of extracting image features, so that the extracted image features have more noise; secondly, in order to solve the problem of different shooting angles and pedestrian posture changes, Most methods adopt a priori part matching strategy. After the parts are consistent, the features of the same part are extracted for matching. The effect is quite significant, but at the same time, the matching of fixed parts may lead to the loss of significant information of other non-matching parts.

A part of the salient information is lost, so that the re-identification performance is greatly reduced in distinguishing some images with obvious differences in non-priori parts.

Images