论文标题
ACRNET：用于多视图实时3D人姿势估计的注意立方体回归网络
ACRNet: Attention Cube Regression Network for Multi-view Real-time 3D Human Pose Estimation in Telemedicine
论文作者
论文摘要
远程医疗中3D骨骼重建的人姿势估计（HPE）长期以来一直受到关注。尽管深度学习的发展使远程医疗中的HPE方法更简单，更易于使用，但解决较低的准确性和高潜伏期仍然是一个巨大的挑战。在本文中，我们提出了一个新型的多视图Cuse Cube回归网络（ACRNET），该网络通过在每个立方体表面上汇总信息的注意点来实时回归关节的3D位置。更特别地，首先创建一个具有特定坐标值的立方体，其每个表面都包含具有特定坐标值的均匀分布的注意点，以从主视图中包装目标。然后，我们的网络通过将加权后每个表面上的注意点的坐标求和来回归每个关节的3D位置。为了验证我们的方法，我们首先在开源ITOP数据集上测试了ACRNET；同时，我们在Trunk Support Trainer（Trust）上收集了一个新的多视图上身运动数据集（UBM），以验证我们在实际康复方案中模型的能力。实验结果证明了与其他最先进方法相比，ACRNET的优势。我们还验证了Acrnet中每个模块的功效。此外，我们的工作分析了医疗监测指标下的ACRNET的性能。由于精度和运行速度高，我们的型号适用于实时远程医疗设置。源代码可在https://github.com/bocehu/acrnet上获得
Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine has long received attention. Although the development of deep learning has made HPE methods in telemedicine simpler and easier to use, addressing low accuracy and high latency remains a big challenge. In this paper, we propose a novel multi-view Attention Cube Regression Network (ACRNet), which regresses the 3D position of joints in real time by aggregating informative attention points on each cube surface. More specially, a cube whose each surface contains uniformly distributed attention points with specific coordinate values is first created to wrap the target from the main view. Then, our network regresses the 3D position of each joint by summing and averaging the coordinates of attention points on each surface after being weighted. To verify our method, we first tested ACRNet on the open-source ITOP dataset; meanwhile, we collected a new multi-view upper body movement dataset (UBM) on the trunk support trainer (TruST) to validate the capability of our model in real rehabilitation scenarios. Experimental results demonstrate the superiority of ACRNet compared with other state-of-the-art methods. We also validate the efficacy of each module in ACRNet. Furthermore, Our work analyzes the performance of ACRNet under the medical monitoring indicator. Because of the high accuracy and running speed, our model is suitable for real-time telemedicine settings. The source code is available at https://github.com/BoceHu/ACRNet
