Pedestrian re-identification method based on double constraint metric learning and sample reordering

Abstract

The invention discloses a pedestrian re-identification method based on double constraint metric learning and sample reordering. The method comprises two stages of training and testing; the training stage comprises the following steps: establishing a cross-camera correlation constraint; establishing a same-camera correlation constraint; and solving a metric matrix; the testing stage comprises the following steps: using the metric matrix to perform feature space projection; calculating the Euclidean distance of query pictures and candidate pictures in a feature space; calculating the initial ordering of the candidate pictures; selecting the first K candidate pictures in a ordering queue; constructing a probabilistic hypergraph by using the relevance of the first K candidate pictures in the feature space; calculating a reordering result based on the probabilistic hypergraph; and returning the final ordering of the candidate pictures. The pedestrian re-identification method based on the double constraint metric learning and sample reordering provided by the invention considers two correlation constraints of training samples simultaneously, so that a feature space obtained by learning is more suitable for pedestrian re-identification, and at the same time, the relevance of the candidate pictures is used to reorder, so that a more accurate pedestrian re-identification result is obtained.

Description

technical field [0001] The invention relates to a method in the technical field of video image processing, in particular to a pedestrian re-identification method based on double-constrained metric learning and sample reordering. Background technique [0002] Video surveillance provides a rich source of information for security early warning, investigation and evidence collection, and suspect tracking. However, the monitoring range of a single camera is very limited, so it is impossible to carry out all-round monitoring of larger or more complex scenes (such as train stations, airports, campuses, etc.). In order to capture more comprehensive and extensive information in public areas, a large number of surveillance cameras are usually required to work together. The traditional video processing technology is mainly designed for a single camera. When the pedestrian target moves out of the current video, it is impossible to determine the whereabouts of the target. Therefore, ho...

AI Technical Summary

Problems solved by technology

However, the existing metric learning algorithms only consider the cross-camera correlation information between pedestrian images under different cameras during the training process, while ignoring the correlation between different pedestrian images within the same camera.

At the same time, the metric learning algorithm is prone to overfitting on the training set, and in the test phase, relying entirely on the learned distance metric matrix for similarity ranking may result in suboptimal pedestrian re-identification results.

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