论文标题
绘制未来：使用基于图的活动表示的活动和下一个活动对象预测
Graphing the Future: Activity and Next Active Object Prediction using Graph-based Activity Representations
论文作者
论文摘要
我们为视频中人类对象相互作用的视觉预测提供了一种新颖的方法。我们旨在预测（a）正在进行的人类对象相互作用的类别以及（b）下一个活动对象（s）（s）（naos）的类别（即，即在不久的将来的交互作用中会发生这种交互作用），而不是预测（a）正在进行的人类对象相互作用的类别以及（b）与对象相互作用的类别。图形匹配依赖于有效的图表编辑距离（GED）方法。使用两个包含人类对象相互作用的视频数据集（即MSR日常活动和CAD120）进行了对拟议方法的实验评估。对于动作预测和NAO预测，获得了高预测精度。
We present a novel approach for the visual prediction of human-object interactions in videos. Rather than forecasting the human and object motion or the future hand-object contact points, we aim at predicting (a)the class of the on-going human-object interaction and (b) the class(es) of the next active object(s) (NAOs), i.e., the object(s) that will be involved in the interaction in the near future as well as the time the interaction will occur. Graph matching relies on the efficient Graph Edit distance (GED) method. The experimental evaluation of the proposed approach was conducted using two well-established video datasets that contain human-object interactions, namely the MSR Daily Activities and the CAD120. High prediction accuracy was obtained for both action prediction and NAO forecasting.
