Learning Visual Obstacle Detection Using Color Histogram Features

Perception of the environment is crucial in terms of successfully playing soccer. Especially the detection of other players improves game play skills, such as obstacle avoidance and path planning. Such information can help refine reactive behavioral strategies, and is conducive to team play capabilities. Robot detection in the RoboCup Standard Platform League is particularly challenging as the Nao robots are limited in computing resources and their appearance is predominantly white in color like the field lines.

This paper describes a vision-based multilevel approach which is integrated into the B-Human Software Framework and evaluated in terms of speed and accuracy. On the basis of color segmented images, a feed-forward neural network is trained to discriminate between robots and non-robots. The presented algorithm initially extracts image regions which potentially depict robots and prepares them for classification. Preparation comprises calculation of color histograms as well as linear interpolation in order to obtain network inputs of a specific size. After classification by the neural network, a position hypothesis is generated.