Robust color segmentation for the RoboCup domain

Abstract

Color segmentation is crucial in robotic applications, such as RoboCup, where the relevant objects can be distinguished by their color. In these applications, real-time performance and robustness are primary concerns. We present a hybrid method for color segmentation based on seeded region growing (SRG) in which the initial seeds are provided by a conservative threshold color segmentation. The key to the robustness of our approach is to use multiple seeds to perform local blob growing, and then merge blobs that belong to the same region. We have implemented our technique on a team of Sony AIBO 4-legged robots, and have successfully tested it in the RoboCup 2001 competition.