Automatic initialization for body tracking: using appearance to learn a model for tracking human upper body motions

Abstract

Social robots require the ability to communicate and recognize the intention of a human interaction partner. Humans commonly make use of gestures for interactive purposes. For a social robot, recognition of gestures is therefore a necessary skill. As a common intermediate step, the pose of an individual is tracked over time making use of a body model. The acquisition of a suitable body model, i.e. self-starting the tracker, however, is a complex and challenging task. This paper presents an approach to facilitate the acquisition of the body model during interaction. Taking advantage of a robust face detection algorithm provides the opportunity for automatic and markerless acquisition of a 3D body model using a monocular color camera. For the given human robot interaction scenario, a prototype has been developed for a single user configuration. It provides automatic initialization and failure recovery of a 3D body tracker based on head and hand detection information, delivering promising results.