College of Computer Science and Technology, Jilin University, Changchun, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
PLoS One. 2022 Feb 23;17(2):e0264302. doi: 10.1371/journal.pone.0264302. eCollection 2022.
The cross-view 3D human pose estimation model has made significant progress, it better completed the task of human joint positioning and skeleton modeling in 3D through multi-view fusion method. The multi-view 2D pose estimation part of this model is very important, but its training cost is also very high. It uses some deep learning networks to generate heatmaps for each view. Therefore, in this article, we tested some new deep learning networks for pose estimation tasks. These deep networks include Mobilenetv2, Mobilenetv3, Efficientnetv2 and Resnet. Then, based on the performance and drawbacks of these networks, we built multiple deep learning networks with better performance. We call our network in this article LHPE-nets, which mainly includes Low-Span network and RDNS network. LHPE-nets uses a network structure with evenly distributed channels, inverted residuals, external residual blocks and a framework for processing small-resolution samples to achieve training saturation faster. And we also designed a static pose sample simplification method for 3D pose data. It implemented low-cost sample storage, and it was also convenient for models to read these samples. In the experiment, we used several recent models and two public estimation indicators. The experimental results show the superiority of this work in fast start-up and network lightweight, it is about 1-5 epochs faster than the Resnet-34 during training. And they also show the accuracy improvement of this work in estimating different joints, the estimated performance of approximately 60% of the joints is improved. Its performance in the overall human pose estimation exceeds other networks by more than 7mm. The experiment analyzes the network size, fast start-up and the performance in 2D and 3D pose estimation of the model in this paper in detail. Compared with other pose estimation models, its performance has also reached a higher level of application.
跨视图 3D 人体姿态估计模型取得了重大进展，它通过多视图融合方法更好地完成了 3D 中人体关节定位和骨骼建模任务。该模型的多视图 2D 姿态估计部分非常重要，但训练成本也非常高。它使用一些深度学习网络为每个视图生成热图。因此，在本文中，我们测试了一些新的用于姿态估计任务的深度学习网络。这些深度网络包括 Mobilenetv2、Mobilenetv3、Efficientnetv2 和 Resnet。然后，基于这些网络的性能和缺点，我们构建了多个具有更好性能的深度学习网络。我们将本文中的网络称为 LHPE-nets，它主要包括 Low-Span 网络和 RDNS 网络。LHPE-nets 使用具有均匀分布通道、倒置残差、外部残差块和处理小分辨率样本的框架的网络结构，以更快地实现训练饱和。我们还设计了一种用于 3D 姿态数据的静态姿态样本简化方法。它实现了低成本样本存储，并且还方便模型读取这些样本。在实验中，我们使用了几个最近的模型和两个公共估计指标。实验结果表明了该工作在快速启动和网络轻量化方面的优越性，它在训练过程中比 Resnet-34 快 1-5 个 epoch。它们还表明了该工作在估计不同关节方面的准确性提高，大约 60%的关节的估计性能得到了提高。它在整体人体姿态估计方面的性能超过了其他网络超过 7mm。实验详细分析了本文模型的网络大小、快速启动以及 2D 和 3D 姿态估计性能。与其他姿态估计模型相比，它的性能也达到了更高的应用水平。
