论文标题
vi-net：人类运动评估的观察不变质量
VI-Net: View-Invariant Quality of Human Movement Assessment
论文作者
论文摘要
我们提出了一种不依赖骨骼数据的人类运动质量评估人类运动质量的观察方法。我们的端到端卷积神经网络由两个阶段组成，首先是从RGB图像中生成每个身体关节的视图轨迹描述仪，然后由RGB图像产生，所有关节的轨迹集合通过适应性，预先培训的2D CNN（例如VGG-19或Resnext-50）进行了处理，以衡量各种体验，以换取各种体验，以获取不同的体育范围，并提供了一个不同的体验。我们发布了唯一的公共可用，多视图，非骨骼，非MOCAP，康复运动数据集（QMAR），并为该数据集中的交叉对象和跨视图方案提供结果。我们表明，VI-NET仅在两次视图上接受训练时，在交叉主题上达到0.66的平均等级相关性为0.66，在看不见的视图上达到了0.65。我们还评估了单视康复数据集Kimore的提议方法，并获得0.66等级相关性，而基线为0.62。
We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D CNN (e.g. VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
