Department of New Media Art, Taipei National University of the Arts, Taipei 112, Taiwan.
Computer Center, Taipei National University of the Arts, Taipei 112, Taiwan.
Sensors (Basel). 2019 Mar 22;19(6):1425. doi: 10.3390/s19061425.
In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players' behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset.
本文提出了一个初步的棒球运动员行为分类系统。通过使用多个物联网传感器和摄像机，该方法通过分析来自异构传感器的信号，准确识别了许多棒球运动员的行为。本文的贡献有三点：（i）获取和分割深度相机和多个惯性传感器的信号，（ii）使用来自深度相机的时变骨架向量投影和从惯性传感器提取的统计特征作为特征，以及（iii）提出了一种基于深度学习的方案来训练行为分类器。实验结果表明，与提出的数据集相比，所提出的深度学习行为系统的准确率超过 95%。
