# Skeleton-based Relational Reasoning for Group Activity Analysis

arXiv: https://arxiv.org/abs/2011.05653 · HTML: https://mlsanity.com/paper/2011.05653/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality](https://mlsanity.com/paper/2112.05892/) (2021)
- [Pose is all you need: The pose only group activity recognition system (POGARS)](https://mlsanity.com/paper/2108.04186/) (2021)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction](https://mlsanity.com/paper/2208.05470/) (2022)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph](https://mlsanity.com/paper/2407.19497/) (2024)
- [M3Act: Learning from Synthetic Human Group Activities](https://mlsanity.com/paper/2306.16772/) (2023)
- [SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball Tactic](https://mlsanity.com/paper/2503.06522/) (2025)
- [DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image](https://mlsanity.com/paper/2303.06439/) (2023)
- [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)
- [Group Activity Recognition via Dynamic Composition and Interaction](https://mlsanity.com/paper/2305.05583/) (2023)
- [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://mlsanity.com/paper/2307.13541/) (2023)
- [ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality](https://mlsanity.com/paper/2407.19820/) (2024)
- [PromptGAR: Flexible Promptive Group Activity Recognition](https://mlsanity.com/paper/2503.08933/) (2025)
- [Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning](https://mlsanity.com/paper/2604.04467/) (2026)

## Abstract (arXiv)

Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it. With our proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the body joints pair-by-pair. Additionally to the joints relations, we also experiment with the previously unexplored relationship between individuals and relevant objects (e.g. volleyball). The individuals distinct relations are then merged through an attention mechanism, that gives more importance to those individuals more relevant for distinguishing the group activity. We evaluate our method in the Volleyball dataset, obtaining competitive results to the state-of-the-art. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.