# F2SD: A dataset for end-to-end group detection algorithms

arXiv: https://arxiv.org/abs/2211.11001 · HTML: https://mlsanity.com/paper/2211.11001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 20 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [F-formation Detection: Individuating Free-standing Conversational Groups in Images](https://mlsanity.com/paper/1409.2702/) (2014)
- [REFORM: Recognizing F-formations for Social Robots](https://mlsanity.com/paper/2008.07668/) (2020)
- [Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants](https://mlsanity.com/paper/1907.12910/) (2019)

## Abstract (arXiv)

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.