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Communication Dans Un Congrès Année : 2024

Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition

Julien Cumin
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Fano Ramparany
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Résumé

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.
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Dates et versions

hal-04538267 , version 1 (09-04-2024)

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  • HAL Id : hal-04538267 , version 1

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Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz. Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition. Context and Activity Modeling and Recognition (CoMoReA) Workshop at IEEE International Conference on Pervasive Computing and Communications (PerCom 2024), Mar 2024, Biarritz, France. ⟨hal-04538267⟩
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