A metric learning method and system for person re-identification

Abstract

The invention discloses a metric learning method and system for pedestrian re-identification, wherein the implementation of the method includes: collecting feature vectors of pedestrian targets under two cameras, establishing a positive sample pair feature vector set and a negative sample pair feature vector set, and calculating positive , The distance between the negative sample and the feature vector, the positive sample is constrained by a single threshold value on the feature vector, the negative sample is constrained by double thresholds on the feature vector, and the distance constraint between the positive and negative samples on the feature vector is established based on the metric matrix. Loss function, iteratively updating the metric matrix with the goal of minimizing the loss function value, this method can effectively reduce the impact of irrelevant variables such as image background and noise on matrix learning, thereby avoiding the occurrence of over-fitting phenomenon, and the obtained metric matrix Strong generalization ability.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and more particularly relates to a metric learning method and system for pedestrian re-identification. Background technique [0002] Pedestrian re-identification algorithm is one of the important fields of image processing and pattern recognition research, focusing on the recognition of specific target pedestrians under cameras without public view. Due to limitations such as video clarity, it is difficult to find the same target through intuitive information such as faces. Instead, we focus on the feature expression based on the appearance of pedestrians, mainly including information such as the color and texture of pedestrian images, and then find a suitable measurement. The method makes the cross-camera features of the same pedestrian target as similar as possible, and the difference of heterogeneous target features is as significant as possible. Since the same target is affected b...

AI Technical Summary

Problems solved by technology

[0003] The present invention proposes a metric learning method for pedestrian re-identification. The purpose is to provide a metric learning method that imposes single-threshold constraints on the feature vectors for positive samples and double-threshold constraints for negative samples on feature vectors. Overfitting problem in complex scenes

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