# F-formation Detection: Individuating Free-standing Conversational Groups in Images

arXiv: https://arxiv.org/abs/1409.2702 · HTML: https://mlsanity.com/paper/1409.2702/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2014.

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

- [ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild](https://mlsanity.com/paper/2205.05177/) (2022)
- [Real-time Trajectory-based Social Group Detection](https://mlsanity.com/paper/2304.05678/) (2023)
- [Dynamic Group Detection using VLM-augmented Temporal Groupness Graph](https://mlsanity.com/paper/2509.04758/) (2025)
- [Detecting socially interacting groups using f-formation: A survey of taxonomy, methods, datasets, applications, challenges, and future research directions](https://mlsanity.com/paper/2108.06181/) (2021)
- [GROWL: Group Detection With Link Prediction](https://mlsanity.com/paper/2111.04397/) (2021)
- [Conversation Group Detection With Spatio-Temporal Context](https://mlsanity.com/paper/2206.02559/) (2022)
- [Learning Social Cost Functions for Human-Aware Path Planning](https://mlsanity.com/paper/2407.10547/) (2024)
- [Socially Pertinent Robots in Gerontological Healthcare](https://mlsanity.com/paper/2404.07560/) (2024)
- [Covy: An AI-powered Robot with a Compound Vision System for Detecting Breaches in Social Distancing](https://mlsanity.com/paper/2207.06847/) (2022)
- [F2SD: A dataset for end-to-end group detection algorithms](https://mlsanity.com/paper/2211.11001/) (2022)
- [Retail store customer behavior analysis system: Design and Implementation](https://mlsanity.com/paper/2309.03232/) (2023)
- [Exploiting temporal information to detect conversational groups in videos and predict the next speaker](https://mlsanity.com/paper/2408.16380/) (2024)
- [Gaze-Anchored Social Net: Decoding Implicit Relations via Joint Modeling](https://mlsanity.com/paper/2607.22847/) (2026)
- [COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention](https://mlsanity.com/paper/2607.28649/) (2026)
- [Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention](https://mlsanity.com/paper/2608.24580/) (2026)
- [Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction](https://mlsanity.com/paper/2609.28467/) (2026)
- [Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features](https://mlsanity.com/paper/2610.18627/) (2026)

## Abstract (arXiv)

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.