# Group Activity Prediction with Sequential Relational Anticipation Model

arXiv: https://arxiv.org/abs/2008.02441 · HTML: https://mlsanity.com/paper/2008.02441/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [OpenTAL: Towards Open Set Temporal Action Localization](https://mlsanity.com/paper/2203.05114/) (2022)
- [Entry-Flipped Transformer for Inference and Prediction of Participant Behavior](https://mlsanity.com/paper/2207.06235/) (2022)
- [Interpretable Deep Feature Propagation for Early Action Recognition](https://mlsanity.com/paper/2107.05122/) (2021)
- [Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction](https://mlsanity.com/paper/2107.05140/) (2021)
- [An end-to-end multi-scale network for action prediction in videos](https://mlsanity.com/paper/2301.01216/) (2022)

## Abstract (arXiv)

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.