Soccer Field Boundary Detection Using Convolutional Neural Networks

Detecting the field boundary is often one of the first steps in the vision pipeline of soccer robots. Conventional methods make use of a (possibly adaptive) green classifier, selection of boundary points and possibly model fitting. We present an approach to predict the coordinates of the field boundary column-wise in the image using a convolutional neural network. This is combined with a method to let the network predict the uncertainty of its output, which allows to fit a line model in which columns are weighted according to the network’s confidence. Experiments show that the resulting models are accurate enough in different lighting conditions as well as real-time capable. Code and data are available online ( https://github.com/bhuman/DeepFieldBoundary , https://sibylle.informatik.uni-bremen.de/public/datasets/fieldboundary ).