Group Activity Prediction with Sequential Relational Anticipation Model
This work addresses group activity prediction for video analysis, offering an incremental advance by focusing on relational evolution.
The paper tackles the problem of predicting group activities from incomplete early frames by modeling relational dynamics, and it achieves significant performance improvements over state-of-the-art methods on two datasets.
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.