The Modelling of Movement of Multiple Animals that Share Behavioural Features

Abstract

Abstract In this work, we propose a model that can be used to infer the behaviour of multiple animals. Our proposal is defined as a set of hidden Markov models that are based on the sticky hierarchical Dirichlet process, with a shared base-measure, and a step and turn with an attractive point (STAP) emission distribution. The latent classifications are representative of the behaviour assumed by the animals, which is described by the STAP parameters. Given the latent classifications, the animals are independent. As a result of the way we formalize the distribution over the STAP parameters, the animals may share, in different behaviours, the set or a subset of the parameters, thereby allowing us to investigate the similarities between them. The hidden Markov models, based on the Dirichlet process, allow us to estimate the number of latent behaviours for each animal, as a model parameter. This proposal is motivated by a real data problem, where the global positioning system (GPS) coordinates of six Maremma Sheepdogs have been observed. Among the other results, we show that four dogs share most of the behaviour characteristics, while two have specific behaviours.