# Group Activity Detection from Trajectory and Video Data in Soccer

arXiv: https://arxiv.org/abs/2004.10299 · HTML: https://mlsanity.com/paper/2004.10299/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection](https://mlsanity.com/paper/2106.14447/) (2021)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling](https://mlsanity.com/paper/2102.03291/) (2021)
- [Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher](https://mlsanity.com/paper/2206.05717/) (2022)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach](https://mlsanity.com/paper/2408.16638/) (2024)
- [Puck localization and multi-task event recognition in broadcast hockey videos](https://mlsanity.com/paper/2105.10563/) (2021)
- [Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS)](https://mlsanity.com/paper/2209.00451/) (2022)
- [A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games](https://mlsanity.com/paper/2108.11149/) (2021)
- [DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera](https://mlsanity.com/paper/2105.09880/) (2021)
- [Learning Football Body-Orientation as a Matter of Classification](https://mlsanity.com/paper/2106.00359/) (2021)
- [Multi-Modal Soccer Scene Analysis with Masked Pre-Training](https://mlsanity.com/paper/2512.19528/) (2025)
- [A Scale-Invariant Trajectory Simplification Method for Efficient Data Collection in Videos](https://mlsanity.com/paper/2304.01340/) (2023)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentation](https://mlsanity.com/paper/2508.10281/) (2025)

## Abstract (arXiv)

Group activity detection in soccer can be done by using either video data or player and ball trajectory data. In current soccer activity datasets, activities are labelled as atomic events without a duration. Given that the state-of-the-art activity detection methods are not well-defined for atomic actions, these methods cannot be used. In this work, we evaluated the effectiveness of activity recognition models for detecting such events, by using an intuitive non-maximum suppression process and evaluation metrics. We also considered the problem of explicitly modeling interactions between players and ball. For this, we propose self-attention models to learn and extract relevant information from a group of soccer players for activity detection from both trajectory and video data. We conducted an extensive study on the use of visual features and trajectory data for group activity detection in sports using a large scale soccer dataset provided by Sportlogiq. Our results show that most events can be detected using either vision or trajectory-based approaches with a temporal resolution of less than 0.5 seconds, and that each approach has unique challenges.