Using Recognition to Guide a Robot's Attention

In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for possible interactions. This is demonstrated for instances that have never been observed before, and under partial occlusion and against cluttered backgrounds. Our approach builds on the Implicit Shape Model of Leibe and Schiele, and extends it to couple recognition to the provision of meta-data useful for a task. Meta-data can for instance consist of part labels or depth estimates. We present experimental results on wheelchairs and cars.