Generative Resident Separation and Multi-label Classification for
  Multi-person Activity Recognition
Xi Chen Julien Cumin Fano Ramparany Dominique Vaufreydaz
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.