Human behavior recognition based on residual generative adversarial networks

Hongbin Tu; Tianhua Zhan; Dongliang Hu

doi:10.1117/12.2641010

2 December 2022 Human behavior recognition based on residual generative adversarial networks

Hongbin Tu, Tianhua Zhan, Dongliang Hu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122880T (2022) https://doi.org/10.1117/12.2641010
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In order to improve the accuracy of human action recognition, a Residual Generative Adversarial Network (Res-GAN) method is proposed by combining the characteristics of Generative Adversarial Networks and Residual Networks. The generator uses an improved residual block extraction feature to obtain deep texture information and enhance the regularization of the model; residual blocks construct the discriminator. On the one hand, the accuracy of image feature extraction is improved, and the convergence speed of the network is accelerated. On the other hand, the discriminator performs the classification pre-training of human behavior recognition while distinguishing true and false samples. In order to avoid the influence of residual generative adversarial network generator on the recognition accuracy due to the instability of the reconstructed samples, the trained residual generative adversarial network discriminator was constructed as a classifier for human behavior recognition. Experiments on the KTH dataset demonstrate the effectiveness of the proposed method.

Citation Download Citation

Hongbin Tu, Tianhua Zhan, and Dongliang Hu "Human behavior recognition based on residual generative adversarial networks", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122880T (2 December 2022); https://doi.org/10.1117/12.2641010

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