Environment adaption for companion-systems

Abstract

One of the key characteristics of a Companion-System is the adaptation of its functionality to the user’s preferences and the environment. On the one hand, a dynamic environment model facilitates the adaption of output modalities in human computer interaction (HCI) to the current situation. On the other hand, continuous tracking of users in the proximity of the system allows for resuming a previously interrupted interaction. Thus, an environment perception system based on a robust multi-object tracking algorithm is required to provide these functionalities. In typical Companion-System applications, persons in the proximity are closely spaced, which leads to statistical dependencies in their behavior. The multi-object Bayes filter allows for modeling these statistical dependencies by representing the multi-object state using random finite sets. Based on the social force model and the knowledge base of the companion system, an approach to modeling object interactions is presented. In this work, the interaction model is incorporated into the prediction step of the sequential Monte Carlo (SMC) of the multi-object Bayes filter. Further, an alternative implementation of the multi-object Bayes filter based on labeled random finite sets is outlined.