# Real-time Trajectory-based Social Group Detection

arXiv: https://arxiv.org/abs/2304.05678 · HTML: https://mlsanity.com/paper/2304.05678/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos](https://mlsanity.com/paper/2007.02632/) (2020)
- [JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection](https://mlsanity.com/paper/2106.08827/) (2021)
- [F-formation Detection: Individuating Free-standing Conversational Groups in Images](https://mlsanity.com/paper/1409.2702/) (2014)
- [Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses](https://mlsanity.com/paper/1803.08071/) (2018)
- [Panoramic Human Activity Recognition](https://mlsanity.com/paper/2203.03806/) (2022)
- [Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration](https://mlsanity.com/paper/2105.14683/) (2021)
- [Self-supervised Social Relation Representation for Human Group Detection](https://mlsanity.com/paper/2203.03843/) (2022)
- [Let me join you! Real-time F-formation recognition by a socially aware robot](https://mlsanity.com/paper/2008.10078/) (2020)
- [I can attend a meeting too! Towards a human-like telepresence avatar robot to attend meeting on your behalf](https://mlsanity.com/paper/2006.15647/) (2020)
- [MeetSense: A Lightweight Framework for Group Identification using Smartphones](https://mlsanity.com/paper/1804.05055/) (2018)

## Built on by (most cited first, 4 of 4)

- [GSON: A Group-based Social Navigation Framework with Large Multimodal Model](https://mlsanity.com/paper/2409.18084/) (2024)
- [Dynamic Group Detection using VLM-augmented Temporal Groupness Graph](https://mlsanity.com/paper/2509.04758/) (2025)
- [Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning](https://mlsanity.com/paper/2404.05578/) (2024)
- [Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction](https://mlsanity.com/paper/2609.28467/) (2026)

## Abstract (arXiv)

Social group detection is a crucial aspect of various robotic applications, including robot navigation and human-robot interactions. To date, a range of model-based techniques have been employed to address this challenge, such as the F-formation and trajectory similarity frameworks. However, these approaches often fail to provide reliable results in crowded and dynamic scenarios. Recent advancements in this area have mainly focused on learning-based methods, such as deep neural networks that use visual content or human pose. Although visual content-based methods have demonstrated promising performance on large-scale datasets, their computational complexity poses a significant barrier to their practical use in real-time applications. To address these issues, we propose a simple and efficient framework for social group detection. Our approach explores the impact of motion trajectory on social grouping and utilizes a novel, reliable, and fast data-driven method. We formulate the individuals in a scene as a graph, where the nodes are represented by LSTM-encoded trajectories and the edges are defined by the distances between each pair of tracks. Our framework employs a modified graph transformer module and graph clustering losses to detect social groups. Our experiments on the popular JRDBAct dataset reveal noticeable improvements in performance, with relative improvements ranging from 2% to 11%. Furthermore, our framework is significantly faster, with up to 12x faster inference times compared to state-of-the-art methods under the same computation resources. These results demonstrate that our proposed method is suitable for real-time robotic applications.