论文标题
二元运动在保存条件下的同步估计
Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions
论文作者
论文摘要
运动同步是指互动人的动作之间的动态时间联系。运动同步的应用是广泛而广泛的。例如，作为对队友之间协调的衡量标准，体育中经常报告同步分数。自闭症社区还将运动同步视为儿童社会和发展成就的关键指标。一般而言，原始视频录制通常用于运动同步估计，这可能会揭示人们的身份。此外，这种隐私问题也阻碍了数据共享，这是自闭症研究不同方法之间公平比较的主要障碍。为了解决这个问题，本文提出了一种用于运动同步估计的合奏方法，这是在隐私保护条件下进行自动运动同步评估的第一个基于深度学习的方法之一。我们的方法完全依赖于可公开共享的身份不足的二级数据，例如骨架数据和光流。我们在两个数据集上验证了我们的方法：（1）从自闭症治疗干预措施中收集的PT13数据集以及（2）从同步潜水竞赛中收集的TASD-2数据集。在这种情况下，我们的方法的表现优于其对应方法，包括深层神经网络和替代方案。
Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The applications of movement synchrony are wide and broad. For example, as a measure of coordination between teammates, synchrony scores are often reported in sports. The autism community also identifies movement synchrony as a key indicator of children's social and developmental achievements. In general, raw video recordings are often used for movement synchrony estimation, with the drawback that they may reveal people's identities. Furthermore, such privacy concern also hinders data sharing, one major roadblock to a fair comparison between different approaches in autism research. To address the issue, this paper proposes an ensemble method for movement synchrony estimation, one of the first deep-learning-based methods for automatic movement synchrony assessment under privacy-preserving conditions. Our method relies entirely on publicly shareable, identity-agnostic secondary data, such as skeleton data and optical flow. We validate our method on two datasets: (1) PT13 dataset collected from autism therapy interventions and (2) TASD-2 dataset collected from synchronized diving competitions. In this context, our method outperforms its counterpart approaches, both deep neural networks and alternatives.
