Fast and Precise Black and White Ball Detection for RoboCup Soccer

Jacob Menashe, Josh Kelle, Katie Genter, Josiah Hanna, Elad Liebman, Sanmit Narvekar, Ruohan Zhang, Peter Stone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In 2016, UT Austin Villa claimed the Standard Platform League's second place position at the RoboCup International Robot Soccer Competition in Leipzig, Germany as well as first place at both the RoboCup US Open in Brunswick, USA and the World RoboCup Conference in Beijing, China. This paper describes some of the key contributions that led to the team's victories with a primary focus on our techniques for identifying and tracking black and white soccer balls. UT Austin Villa's ball detection system was overhauled in order to transition from the league's bright orange ball, used every year of the competition prior to 2016, to the truncated icosahedral pattern commonly associated with soccer balls.

We evaluated and applied a series of heuristic region-of-interest identification techniques and supervised machine learning methods to produce a ball detector capable of reliably detecting the ball’s position with no prior knowledge of the ball’s position. In 2016, UT Austin Villa suffered only a single loss which occurred after regulation time during a penalty kick shootout. We attribute much of UT Austin Villa’s success in 2016 to our robots’ effectiveness at quickly and consistently localizing the ball.

In this work we discuss the specifics of UT Austin Villa’s ball detector implementation which are applicable to the specific problem of ball detection in RoboCup, as well as to the more general problem of fast and precise object detection in computationally constrained domains. Furthermore we provide empirical analyses of our approach to support the conclusion that modern deep learning techniques can enhance visual recognition tasks even in the face of these computational constraints.
Original languageEnglish
Title of host publicationRoboCup 2017: Robot World Cup XXI
EditorsHidehisa Akiyama, Oliver Obst, Claude Sammut, Flavio Tonidandel
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages14
ISBN (Electronic)978-3-030-00308-1
ISBN (Print)978-3-030-00307-4
Publication statusPublished - 7 Sept 2018
Event21st Annual RoboCup International Symposium - Nagoya, Japan
Duration: 31 Jul 201731 Jul 2017
Conference number: 21

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st Annual RoboCup International Symposium
Abbreviated titleRoboCup 2017
Internet address


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