Cross-domain pedestrian re-identification method and system based on multi-feature mixed learning

Abstract

The invention provides a cross-domain pedestrian re-identification method and system based on multi-feature mixed learning, and belongs to the technical field of computer vision. The method comprises: by means of a re-identification model subjected to combined training, extracting pedestrian global features, pedestrian attribute features and pedestrian local features of the pedestrian image to be recognized and a bottom library image, which is similar to the pedestrian identity in the pedestrian image to be recognized, in the image bottom library gallley; and fusing the extracted to-be-identified features, and carrying out similarity matching sorting on the fused features of the features of the bottom library image to obtain a pedestrian re-identification result. According to the method, inter-domain joint training and multi-feature mixed learning are utilized to reduce inter-domain differences, so that the system is more stable and higher in robustness, source domain training of global and local features and joint training of attribute features are performed on images of different scenes, pedestrian attributes are combined, the adaptive capacity of a cross-domain pedestrian re-identification model is improved, and pedestrian re-identification is carried out on a cross-domain data set, so that the cross-domain pedestrian re-identification performance is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a cross-domain pedestrian re-identification method and system based on multi-feature hybrid learning. Background technique [0002] Pedestrian re-identification is the process of matching the same target person from different camera perspectives, which plays an important role in traffic, public security and video surveillance. The task of person re-identification mainly includes two steps: feature extraction and similarity measurement. In the traditional pedestrian re-identification method, the idea of ​​feature extraction is mainly to manually extract some low-level image features, and the accuracy of re-identification is low. In recent years, with the rapid development of convolutional neural networks and deep learning, more and more deep learning models have been applied to the problem of person re-identification. This also makes the performance of pedestrian re-iden...

AI Technical Summary

Problems solved by technology

At present, pedestrian re-identification based on deep learning is very mature in single feature learning, and there are relatively few studies on multi-feature mixed learning methods.

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