Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
<|reference_start|>Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition: This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from different residents. The second model, BiGRU+Q2L, uses a Query2Label multi-label classifier to predict multiple activities simultaneously. Performances of these models are compared to a state-of-the-art model in different experimental scenarios, using a state-of-the-art dataset of two residents in a home instrumented with ambient sensors. These results lead to a discussion on the advantages and drawbacks of resident separation and multi-label classification for multi-person activity recognition.<|reference_end|>
@article{chen2024generative,
    title={Generative Resident Separation and Multi-label Classification for
  Multi-person Activity Recognition},
    author={Xi Chen (LIG), Julien Cumin, Fano Ramparany, Dominique Vaufreydaz
  (LIG)},
    journal={arXiv preprint arXiv:2404.07245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07245},
    primaryClass={cs.LG cs.AI eess.SP}
}