Ben Kröse

Visual surveillance in wide areas (e.g. airports) relies on cameras that observe non-overlapping scenes. Multi-person tracking requires re-identification of a person when he/she leaves one field of view, and later appears at another. For this, we use appearance cues. Under the assumption that all observations of a single person are Gaussian distributed, the observation model in our approach consists of a Mixture of Gaussians. In this paper we propose a distributed approach for learning this MoG, where every camera learns from both its own observations and communication with other cameras. We present the multi-observations newscast EM algorithm for this, which is an adjusted version of the recently developed newscast EM. The presented algorithm is tested on artificial generated data and on a collection of real-world observations gathered by a system of cameras in an office building.

While expressiveness in human communication is a natural and widely observed phenomenon, in studies of humans interacting with robots and screen agents it is relatively unexplored. If it occurs however, this could mean that the artificial personality is accepted as a conversation partner by the user. An experiment with a robot and a screen agent in an eldercare institution both in a more and less expressive condition shows that it occurs: participants showed indeed more expressiveness with a more expressive robot or agent. The effect seemed to be stronger for the robot. Although the robot differed in more ways from the agent, this could be an indication of agent embodiment being a moderating factor.

This study examines the influence of perceived social abilities of a robot on user's attitude towards and acceptance of the robot. An interface robot with simulated conversational capabilities was used in a Wizard of Oz experiment with two conditions: a more socially communicative (the robot made use of a larger set of social abilities in interaction) and a less socially communicative interface. Participants (n=40) were observed in 5 minute interaction sessions and were asked to answer questions on perceived social abilities and technology acceptance. Results show that participants who were confronted with the more socially communicative version of the robot felt more comfortable and were more expressive in communicating with it. This suggests that the more socially communicative condition would be more likely to be accepted as a conversational partner. However, the findings did not show a significant correlation between perceived social abilities and technology acceptance