| dc.description.abstract | Robotic soccer vision has been a major research problem in
RoboCup and, even though many progresses have been made so that,
for example, games now can run without many constraints on the lighting
conditions, the problem has not been completely solved and on-site
camera calibration is always a major activity for RoboCup soccer teams.
While di erent robotic soccer vision and object perception techniques
continue to appear in the RoboCup Soccer League, there is a lack of
quantitative evaluation of existing methods.
Since we believe that a quantitative evaluation of soccer vision algorithms
will led to signi cant advances in the performance on perception
and on the entire soccer task, in this paper we propose a benchmarking
methodology for evaluating robotic soccer vision systems. We discuss
the main issues of a successful benchmarking methodology: (i) a large
and complete data base or data sets with ground truth; (ii) a public
repository with data sets, algorithms and implementations that can be
dynamically updated and (iii) evaluation metrics, error functions and
comparison results. | |
