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arXiv · 2404.07245

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

Xi Chen·Julien Cumin·Fano Ramparany·Dominique Vaufreydaz
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Abstract

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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Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz. 2024-04-10. Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition. https://arxiv.org/abs/2404.07245

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