论文标题
使用动力学骨架功能的基于内存组采样的在线操作识别
Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features
论文作者
论文摘要
在线行动识别是人类以人为中心的智能服务的重要任务，由于人类行动的空间和时间尺度的品种和不确定性，这仍然很难实现。在本文中，我们提出了两个核心想法来处理在线行动识别问题。首先，我们将空间和时间骨骼特征结合在一起来描绘动作，不仅包括几何特征，还包括多规模运动特征，从而涵盖了动作的空间和时间信息。其次，我们提出了一种记忆组抽样方法，以结合先前的动作框架和当前的动作帧，该方法基于以下事实：相邻框架在很大程度上是多余的，并且采样机制可确保还考虑了长期上下文信息。最后，使用采样框架的功能，采用了改进的1D CNN网络进行训练和测试。使用公共数据集与最新方法的比较结果表明，所提出的方法是快速有效的，并且具有竞争性的性能
Online action recognition is an important task for human centered intelligent services, which is still difficult to achieve due to the varieties and uncertainties of spatial and temporal scales of human actions. In this paper, we propose two core ideas to handle the online action recognition problem. First, we combine the spatial and temporal skeleton features to depict the actions, which include not only the geometrical features, but also multi-scale motion features, such that both the spatial and temporal information of the action are covered. Second, we propose a memory group sampling method to combine the previous action frames and current action frames, which is based on the truth that the neighbouring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Finally, an improved 1D CNN network is employed for training and testing using the features from sampled frames. The comparison results to the state of the art methods using the public datasets show that the proposed method is fast and efficient, and has competitive performance
