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One of the main challenges in RoboCup is to maintain a high level of speed and accuracy in decision making and performing actions by the robot players. Although we might be able to use complicated hardware and software on the robots to achieve the desired accuracy, but such systems might not be applicable in real-time RoboCup environment due to their high processing time. This is quite serious for the robots equipped with more than one vision systems.   To reduce the processing time we developed some basic ideas that are inspired by a number of features in the human vision system. These ideas included efficient need-based vision, that reduces the number of objects to be detected to a few objects of interest with the minimum needed accuracy, introduction of static and dynamic regions of interest, which proposes the most probable areas to search for an object of interest, an experimentally reliable method for color segmentation in variable illumination situation, and finally, the usage of some domain specific knowledge that is used in detecting and tracking a unique safe point on the ball.    We have implemented these methods on RoboCup environment and satisfactory results were obtained.

An Efficient Need-Based Vision System in Variable Illumination Environment of Middle Size RoboCup