Pedestrian re-identification method and system based on unsupervised cross visual angle metric learning

Abstract

The invention discloses a pedestrian re-identification method and system based on unsupervised cross visual angle metric learning, and mainly solves the problems that accurate modeling of pedestriansis difficult and a traditional metric learning method needs a large amount of supervised information. The method comprises the following implementation steps: (1) carrying out feature extraction; (2)constructing a common and characteristic projection matrix; (3) carrying out unsupervised cross visual angle measurement learning; (4) constraining consistent distribution of downlink person characteristics of different cameras; (5) optimizing a projection matrix; and (6) projecting the query samples and the test sample set to a potential feature space, and comparing the feature distances to obtain a sorting result of the test sample set. According to the method, pedestrian characteristics are decomposed into common characteristics and characteristic characteristics, so that the pedestrian characteristic expression precision is improved. Meanwhile, the provided unsupervised metric learning method can better meet the actual requirements of a large number of monitoring networks at present, and the method can be applied to the fields of intelligent monitoring, traffic control, criminal investigation assistance and the like.

Description

technical field [0001] The invention belongs to the technical field of information processing, and in particular relates to a pedestrian re-identification technology in a monitoring scene, which can be used in the fields of public safety intelligent monitoring, traffic control, criminal investigation assistance and the like. Background technique [0002] With the development of monitoring technology, more and more cameras are used in security systems. Pedestrian re-identification is a technology to find the same pedestrian from surveillance cameras that do not overlap in space. In a large-scale surveillance network, person re-identification technology is very important for target tracking and behavior analysis. Due to the difference in imaging conditions, the apparent characteristics of pedestrians under different cameras vary greatly, such as brightness, posture, occlusion, etc., which brings great challenges to pedestrian re-identification. [0003] At present, researche...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that it is difficult to accurately model pedestrians in the prior art and the modeling process is time-consuming and laborious, the present invention provides a pedestrian re-identification method based on unsupervised cross-view metric learning, which uses massive unlabeled pedestrian data to learn camera The commonality and characteristics between pedestrians, and then explore the potential relationship between pedestrians

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