The rUNSWift SPL Field Segmentation Dataset
This provides training data for the RoboCup SPL community to address field segmentation challenges, but it is incremental as it offers a new dataset rather than a novel method.
The authors tackled the problem of soccer field segmentation in RoboCup SPL by creating a dataset of 20 videos with high-resolution frames and corresponding labels, recorded under various conditions using Nao robots.
In RoboCup SPL, soccer field segmentation has been widely recognised as one of the most critical robot vision problems. Key challenges include dynamic light condition, different calibration status for individual robot, various camera prospective and more. In this paper, we propose a dataset that contains 20 videos recorded with Nao V5/V6 humanroid robots by team rUNSWift under different circumstances. Each of the videos contains several consecutive high resolution frames and the corresponding labels for field. We propose this dataset to provide training data for the league to overcome field segmentation problem. The dataset will be available online for download. Details of annotation and example of usage will be explained in later sections.