An Adaptive Human Posture Detection Algorithm Based on Generative Adversarial Network

Abstract

Human posture equipment technology has advanced significantly thanks to advances in deep learning and machine vision. Even the most advanced models may not be able to predict all body joints accurately. This paper proposes an adaptive generative adversarial network to improve the human posture detection algorithm in order to address this issue. GAN is used in the algorithm to detect human posture improvement. The algorithm uses OpenPose to detect and connect keypoints and then generates heat maps in the GAN system model. During the training process, the confidence evaluation mechanism is added to the system model. The generator predicts posture, while the resolver refines human joints over time. And, by using normalization technologies in the confidence evaluation mechanism, the generator can pay more attention to the prominent body joints, improving the algorithm's body detection accuracy of nodes. In MPII, LSP, and FLIC datasets, the proposed algorithm has shown to have a good detection effect. Its positioning accuracy is about 95.37 percent, and it can accurately locate the joints of the entire body. Several other algorithms are outperformed by this one. The algorithm described in this article has the best simultaneous runtime in the LSP dataset.