论文标题
轨迹预测的递归社会行为图
Recursive Social Behavior Graph for Trajectory Prediction
论文作者
论文摘要
社交互动是人类轨迹预测的重要话题，以产生合理的途径。在本文中，我们提出了一个基于团体的社会互动模型的新颖见解，以探索行人之间的关系。我们递归提取通过基于小组的注释监督的社会表示形式，并将其提出为社会行为图，称为递归社会行为图。我们的递归机制在很大程度上探讨了表示能力。然后，图形卷积神经网络被用来在该图中传播社会互动信息。在递归社会行为图的指导下，我们在ADE中以11.1％的速度超过了ETH和UCY数据集的最新方法，平均为FDE，并成功预测了复杂的社会行为。
Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formulate them into a social behavior graph, called Recursive Social Behavior Graph. Our recursive mechanism explores the representation power largely. Graph Convolutional Neural Network then is used to propagate social interaction information in such a graph. With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art method on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully predict complex social behaviors.
