Multi-pose pedestrian image synthesis algorithm based on generative adversarial network

Abstract

The invention discloses a multi-pose pedestrian image synthesis algorithm based on a generative adversarial network. The multi-pose pedestrian image synthesis algorithm comprises the following steps:S1, obtaining a training data set and a test data set from a pedestrian re-identification task data set Market-1501; S2, constructing a generative adversarial network model through the training data set according to a preset method; S3, adding an attitude information latent code into the generative adversarial network model input by adopting a preset method; S4, constructing an objective functionof a generative adversarial network model based on the attitude information latent code, and synthesizing a multi-attitude pedestrian image by using the generative adversarial network model with the objective function; and S5, performing experimental result analysis according to the synthesized multi-pose pedestrian image. The multi-pose pedestrian image synthesis algorithm has the beneficial effects that the solution space of the generator is effectively reduced, so that the generative adversarial network training is more stable, and high-quality multi-pose pedestrian pictures can be generated.

Description

technical field [0001] The present invention relates to the technical field of image synthesis algorithms, in particular to a multi-pose pedestrian image synthesis algorithm based on generative confrontation networks. Background technique [0002] Algorithms that generate more realistic-looking, natural-looking images are becoming increasingly popular in the field of computer vision, driven by the growing demand for high-quality synthetic images in real life. And character pose transfer is a very active topic in this field. With the widespread application of deep neural networks in computer vision, various novel generative network structures, such as variational autoencoder networks and generative adversarial networks, have achieved certain achievements in the field of image generation in recent years. [0003] However, most current conditional information-based generative adversarial networks (condition GAN) focus more on the expression of latent codes or image quality, wh...

AI Technical Summary

Problems solved by technology

For the features based on the generative confrontation network proposed by Wasserstein et al., it has the following disadvantages: the generator is far more difficult to train to converge than the discriminator, so the discriminator is easy to converge earlier than the generator, resulting in an overly powerful discriminator, resulting in The confrontation training and learning between the generator and the discriminator cannot be carried out, and it inhibits the generator from learning and imitating the feature space

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