Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
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.
Cite
@article{arxiv.2404.07245,
title = {Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition},
author = {Xi Chen and Julien Cumin and Fano Ramparany and Dominique Vaufreydaz},
journal= {arXiv preprint arXiv:2404.07245},
year = {2024}
}
Comments
Context and Activity Modeling and Recognition (CoMoReA) Workshop at IEEE International Conference on Pervasive Computing and Communications (PerCom 2024), Mar 2024, Biarritz, France