Pedestrian re-identification method based on global and local similarity measure learning

Abstract

A pedestrian re-identification method based on global and local similarity measure learning comprises the following steps: first, all pedestrian images in a pedestrian re-identification database are preprocessed, and the local features and global features of each pedestrian image are extracted; then, in a training module, the overall similarity between two pedestrian samples is measured based on the local similarity and global similarity between the pedestrian samples, and a measure matrix is learned; and finally, in a test module, the measure matrix learned by the training module is imported to measure the similarity between each pedestrian sample to be measured and pedestrian samples in the database, sorting is performed according to the similarity, and the pedestrian samples to be measured are identified.

Description

Technical field: [0001] The invention proposes a method for pedestrian re-identification combined with local and global similarity measurement learning, which relates to the fields of computer vision and pattern recognition. Background technique: [0002] With the development of social economy, people have higher and higher requirements for safety precautions, especially in public places such as railway stations, airports, and large shopping malls. Video surveillance is a product of technological development and is widely used in the field of security protection. However, in the traditional video surveillance system, the monitoring task is done manually, which requires the monitoring personnel to stare at the surveillance video non-stop, which is a great challenge for the monitoring personnel. Pedestrian re-identification is a cross-monitoring pedestrian target recognition technology, which can quickly identify human targets of interest in the monitoring system. [0003] I...

AI Technical Summary

Problems solved by technology

[0003] In most monitoring scenarios, the captured images of pedestrians often have problems such as occlusion, pose changes, illumination changes, and low resolution, making pedestrian re-identification one of the most challenging problems in the field of intelligent video surveillance.

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