Directed attention - a cognitive vision system for a mobile robot

In this paper we introduce a method to combine bottom-up saliency, texture descriptors and top-down attention to direct the focus on arbitrary objects in a human-robot interaction scenario. In a usual bottom-up attention process interesting features are extracted out of the image to focus on salient regions. We expand this approach with a top-down process, which determines in one single image the most discriminative features that separate fore- and background best for the observed context. In order to enable the robot also focussing on complex objects like humans, we propose to use a simple model consisting of multiple regions. Thus, the position of an object can be estimated using a probability distribution generated by the attention process. Finally, the system is unrestricted in terms of scene constraints or object appearance and can deal with the requirements of a mobile robot scenario. The attention system has been evaluated both in a human-robot interaction scenario, directing the attention towards interaction partners and in an object learning scenario, focusing on objects. Our results show that the system performed robustly in different applications without any changes to the algorithm.