In this paper, we propose a novel approach to predict group activities given
the beginning frames with incomplete activity executions. Existing action
prediction approaches learn to enhance the representation power of the partial
observation. However, for group activity prediction, the relation evolution of
people's activity and their positions over time is an important cue for
predicting group activity. To this end, we propose a sequential relational
anticipation model (SRAM) that summarizes the relational dynamics in the
partial observation and progressively anticipates the group representations
with rich discriminative information. Our model explicitly anticipates both
activity features and positions by two graph auto-encoders, aiming to learn a
discriminative group representation for group activity prediction. Experimental
results on two popularly used datasets demonstrate that our approach
significantly outperforms the state-of-the-art activity prediction methods.

本研究提出一种基于序列关系预测模型的组活动预测方法，该模型可以总结部分观测数据中的关系动态并逐步预测具有区分性信息的群体表示，通过两种图形自编码器预测活动特征和位置，并在实验中取得了显著的预测效果。