Exploiting temporal information to detect conversational groups in videos and predict the next speaker
<|reference_start|>Exploiting temporal information to detect conversational groups in videos and predict the next speaker: 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.<|reference_end|>
@article{tosato2024exploiting,
    title={Exploiting temporal information to detect conversational groups in
  videos and predict the next speaker},
    author={Lucrezia Tosato, Victor Fortier, Isabelle Bloch, Catherine Pelachaud},
    journal={Pattern Recognition Letters Volume 177, January 2024, Pages 164
  168},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16380},
    primaryClass={cs.CV}
}