An unsupervised approach for adaptive color segmentation

Abstract

One of the key requirements of robotic vision systems for real-life application is the ability to deal with varying lighting conditions. Many systems rely on color-based object or feature detection using color segmentation. A static approach based on preinitialized calibration data is not likely to perform very well under natural light. In this paper we present an unsupervised approach for color segmentation which is able to self-adapt to varying lighting conditions during run-time. The approach comprises two steps: initialization and iterative tracking of color regions. Its applicability has been tested on vision systems of soccer robots participating in RoboCup tournaments.