Pedestrian re-identification method based on distance centralization and projection vector learning

Abstract

The invention relates to the technical field of pedestrian re-identification in computer vision, in particular to a pedestrian re-identification method based on distance centralization and projectionvector learning. The method includes the following steps: Step 1, dividing a pedestrian training set and test set; Step 2, extracting features of a pedestrian image, including color features and texture features; Step 3, calculating the feature distance of centralization; Step 4, building a pedestrian re-identification model based on iterative projection vector learning; Step 5, using a conjugategradient method to solve the model iteratively; and Step 6, calculating different pedestrian feature distances in the test set to perform pedestrian re-identification, the overfitting situation brought by class imbalance is effectively solved and the identification accuracy of pedestrian re-identification is improved. The pedestrian re-identification method based on distance centralization and projection vector learning can well improve the training speed and has a good suppression effect on the noise. The method has very good robustness to pedestrian posture, illumination variation and shielding.

Description

technical field [0001] The invention relates to the technical field of pedestrian re-identification in computer vision, in particular to a pedestrian re-identification method based on distance centralization and projection vector learning. Background technique [0002] At present, more and more camera systems are widely deployed in public places for 24-hour uninterrupted monitoring, which generates a large amount of video data, making the traditional video monitoring system that mainly relies on manual monitoring and identification not only consumes a lot of manpower , and very ineffectively. Therefore, automatic processing and analysis of video data is of great help to improve the efficiency of video surveillance. In video surveillance, when a pedestrian is captured by a camera located in a public place, that is, after one or more frames of images of the pedestrian are acquired, the process of using the existing camera network to find the place where the target pedestrian ...

AI Technical Summary

Problems solved by technology

This method has low requirements for feature selection, but has some problems such as long training time, large dimension of projection matrix, and easy overfitting.

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