Unsupervised daily routine modelling from a depth sensor using top-down and bottom-up hierarchies

Abstract

A person's routine incorporates the frequent and regular behaviour patterns over a time scale, e.g. daily routine. In this work we present a method for unsupervised discovery of a single person's daily routine within an indoor environment using a static depth sensor. Routine is modelled using top down and bottom up hierarchies, formed from location and silhouette spatio-temporal information. We employ and evaluate stay point estimation and time envelopes for better routine modelling. The method is tested for three individuals modelling their natural activity in an office kitchen. Results demonstrate the ability to automatically discover unlabelled routine patterns related to daily activities as well as discard infrequent events.