# A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition

arXiv: https://arxiv.org/abs/1812.10328 · HTML: https://mlsanity.com/paper/1812.10328/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Pose is all you need: The pose only group activity recognition system (POGARS)](https://mlsanity.com/paper/2108.04186/) (2021)
- [Learning Group Activities from Skeletons without Individual Action Labels](https://mlsanity.com/paper/2105.06754/) (2021)
- [MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition](https://mlsanity.com/paper/2304.08803/) (2023)
- [Group Activity Recognition using Unreliable Tracked Pose](https://mlsanity.com/paper/2401.03262/) (2024)
- [Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating](https://mlsanity.com/paper/2604.10078/) (2026)

## Abstract (arXiv)

In this work, we present a framework based on multi-stream convolutional neural networks (CNNs) for group activity recognition. Streams of CNNs are separately trained on different modalities and their predictions are fused at the end. Each stream has two branches to predict the group activity based on person and scene level representations. A new modality based on the human pose estimation is presented to add extra information to the model. We evaluate our method on the Volleyball and Collective Activity datasets. Experimental results show that the proposed framework is able to achieve state-of-the-art results when multiple or single frames are given as input to the model with 90.50% and 86.61% accuracy on Volleyball dataset, respectively, and 87.01% accuracy of multiple frames group activity on Collective Activity dataset.