Abstract
The behavior for a humanoid robot is often modeled in accordance with human behavior. Current research suggests that analyzing infant behavior as a basis for designing the robot behavior can guide us to a natural robot interface. Based on this idea many researchers support saliency systems as a bottom-up inspired way to simulate infant-like gazing behavior. In the field of saliency systems many different approaches have proposed and quantified in terms of speed, quality and other technical issues. But so far, no one compared and quantified them in terms of natural infant tutor interaction. The question we would like to address in this paper is: Can state-of-the-art saliency systems model infant gazing behavior in tutoring situations? By addressing these issues we want to take a step towards an autonomous robot system, which could be used more natural interaction experiments in future.
Highlights
The employed saliency systems are briefly described in subsection II-B
Random center-surround pattern based Saliency [11] is a methodology that employs a biologically plausible dissimilarity metric which is employed to compute the contrast between any two random pixels on the input image
The percentages describes the matching accuracy of a given saliency system to the child’s eye gazing behavior. It seems that the methodology of Seo and Milanfar [9] has the best matching with the gazing behavior of a 8-11 month old child
Summary
We compared 7 different saliency systems with the gazing behavior of infants between the age of 8-11 months. The infant’s gazing behavior and the interaction structure of the tutoring situation was manually annotated. Based on this annotation we selected images as input for the saliency systems. The interaction data used in the course of this research is described in the following subsections. The employed saliency systems are briefly described in subsection II-B
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