# Exploiting temporal information to detect conversational groups in videos and predict the next speaker

arXiv: https://arxiv.org/abs/2408.16380 · HTML: https://mlsanity.com/paper/2408.16380/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [F-formation Detection: Individuating Free-standing Conversational Groups in Images](https://mlsanity.com/paper/1409.2702/) (2014)
- [Let me join you! Real-time F-formation recognition by a socially aware robot](https://mlsanity.com/paper/2008.10078/) (2020)

## Abstract (arXiv)

Studies in human human interaction have introduced the concept of F formation to describe the spatial arrangement of participants during social interactions. This paper has two objectives. It aims at detecting F formations in video sequences and predicting the next speaker in a group conversation. The proposed approach exploits time information and human multimodal signals in video sequences. In particular, we rely on measuring the engagement level of people as a feature of group belonging. Our approach makes use of a recursive neural network, the Long Short Term Memory (LSTM), to predict who will take the speaker's turn in a conversation group. Experiments on the MatchNMingle dataset led to 85% true positives in group detection and 98% accuracy in predicting the next speaker.