Toward RoboCup without color labeling

Authors:
Robert Hanek;Thorsten Schmitt;Sebastian Buck;Michael Beetz
Affiliations:
Munich University of Technology, Germany;Munich University of Technology, Germany;Department of Computer Science at Munich University of Technology, Germany;Munich University of Technology's Department of Computer Science and head of the Intelligent Autonomous Systems research group
Venue:
AI Magazine
Year:
2003

Citing 1
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Abstract

Object recognition and localization methods in RoboCup work on color-segmented camera images. Unfortunately, color labeling can be applied to object-recognition tasks only in very restricted environments, where different kinds of objects have different colors. To overcome these limitations, we propose an algorithm, called the CONTRACTING CURVE DENSITY (CCD) algorithm, for fitting parametric curves to image data. The method neither assumes object-specific color distributions or specific edge profiles, nor does it need threshold parameters. Hence, no training phase is needed. To separate adjacent regions, we use local criteria that are based on local image statistics. We apply the method to the problem of localizing the ball and show that the CCD algorithm reliably localizes the ball even in the presence of heavily changing illumination, strong clutter, specularity, partial occlusion, and texture.