A Pedestrian Re-Identification Method Based on Global Distance Scale Loss Function

Abstract

The invention discloses a pedestrian re-identification method based on a global distance scale loss function. The specific steps include: performing data enhancement processing on the training data of the pedestrian re-identification data set; randomly selecting each batch of data; constructing a human body component-based Deep neural network and initialize the network; use the cross-entropy loss function and the loss function based on the global distance scale to supervise the training of the deep neural network at the same time; perform feature extraction on the target pedestrian image and pedestrian images in the pedestrian image library, and perform cosine similarity Degree calculation and sorting to get the recognition results. The present invention proposes a loss function of the global distance scale based on statistical characteristics, which can effectively avoid noise interference and reduce the risk of over-fitting, and improve model robustness and generalization ability.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a pedestrian re-identification method based on a global distance scale loss function. Background technique [0002] With the development and advancement of deep learning and the increasing popularity of video surveillance technology, pedestrian re-identification technology has become more and more important because of its ability to search for pedestrians to be found among a large number of pedestrians. [0003] In the process of the development of person re-identification technology from traditional metric learning to deeper and wider deep neural network learning, the measurement of distance is inseparable. Pedestrian re-identification technology can effectively shorten the distance between the same kind and increase the distance between the different kinds in the feature space. Based on distance metrics, researchers have proposed many loss functions for supervised netw...

AI Technical Summary

Problems solved by technology

[0004] However, the loss function based on the distance scale in the existing technology needs to rely on the measurement relationship between the two classes or between classes, and lacks a consideration of using global statistical properties.

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