Sangmyung University, Seoul 03016, Republic of Korea.
Comput Intell Neurosci. 2022 Jun 8;2022:3276696. doi: 10.1155/2022/3276696. eCollection 2022.
With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.
随着图像、视频和其他数据的产生，如何识别视频中的动作步态逐渐成为研究的焦点。针对舞蹈视频图像中动作复杂多变、连贯性强、遮挡严重的问题，本文提出了一种基于 GAN（生成对抗网络）的舞蹈动作步态轮廓动态识别模型。GAN 方法用于将任意状态下的步态图转换为一组具有多个角度的正常状态下的步态图，并依次排列。为了尽可能保留更多的原始特征信息，采用多种损失策略对网络进行优化，增加类间距离，减小类内距离。实验结果表明，该模型在 50°、90°和 120°处的平均识别率分别为 93.24%、98.24%和 97.93%，表明该舞蹈运动识别方法的识别精度较高。并且该方法能够有效提高舞蹈动作步态轮廓的动态识别。
