Target re-identification method and system based on non-supervised pyramid similarity learning

Abstract

The invention belongs to the field of target re-identification, and provides a target re-identification method and system based on non-supervised pyramid similarity learning. The target re-identification method based on non-supervision pyramid similarity learning comprises the steps of obtaining a to-be-queried sample image and a target scene domain image; and outputting a target image matched with the to-be-queried sample image in the target scene domain through the target re-identification model, wherein a training and updating process of the target re-identification model includes the following steps: carrying out non-supervision multi-scale horizontal pyramid similarity learning on images of a source scene domain and a target scene domain; and automatically labeling the target scene domain sample image according to the similarity and screening out a training sample to train and update the initial model to obtain a target re-identification model. Through continuous iterative training and updating, the model is more and more adaptive to sample data in a target scene domain, and the accuracy of pedestrian target re-identification can be improved.

Description

technical field [0001] The invention belongs to the field of target re-identification, in particular to a target re-identification method and system based on non-supervised pyramid similarity learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The purpose of target re-identification is to compare and match the pedestrian target image to be found with the pedestrian images obtained under different cameras, and find out whether the target pedestrian appears in different camera monitoring scenes. This technology plays an important role in intelligent surveillance and public safety. This problem has always been challenging in complex surveillance environments (such as lighting changes, objects occluded by other things, different surveillance perspectives, etc.). [0004] Recently, object re-identification methods based on deep learning...

AI Technical Summary

Problems solved by technology

This problem has always been challenging in complex monitoring environments (such as lighting changes, objects occluded by other things, different monitoring perspectives, etc.)

The performance of these methods is still not as good as the corresponding supervisory method. There are still problems in building models and migration algorithms. Most of them use the overall feature model. When the target is blocked or the monitoring perspective changes, the performance will drop significantly.

[0005] In summary, the inventors found that the target model constructed by the current target re-identification method is inaccurate, and the target model is not suitable for unlabeled sample characteristics

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