论文标题
DeepActsnet：面部，手和身体的空间和运动特征与卷积和图形网络相结合，以改善动作识别
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition
论文作者
论文摘要
现有的动作识别方法主要集中于人体骨骼数据中的关节和骨骼信息，因为它具有对环境的复杂背景和动态特征的鲁棒性。在本文中，我们将人体骨架数据与面部和两只手的空间和运动特征相结合，并呈现“深度动作邮票（DeepActs）”，这是一种新的数据表示，以编码视频序列中的动作。我们还提出了“ DeepActsnet”，这是一个基于深度学习的集合模型，该模型从深度动作邮票中学习卷积和结构特征，以进行高度准确的动作识别。对三个具有挑战性的动作识别数据集（NTU60，NTU120和SYSU）进行的实验表明，与先进的方法相比，使用深层动作邮票训练的拟议模型在动作识别准确性方面具有较低的计算成本，从而产生了相当大的提高。
Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics of the environments. In this paper, we combine body skeleton data with spatial and motion features from face and two hands, and present "Deep Action Stamps (DeepActs)", a novel data representation to encode actions from video sequences. We also present "DeepActsNet", a deep learning based ensemble model which learns convolutional and structural features from Deep Action Stamps for highly accurate action recognition. Experiments on three challenging action recognition datasets (NTU60, NTU120, and SYSU) show that the proposed model trained using Deep Action Stamps produce considerable improvements in the action recognition accuracy with less computational cost compared to the state-of-the-art methods.
