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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, and Peter Stone. Fast and Precise Black and White Ball Detection for RoboCup Soccer. In RoboCup-2017: Robot Soccer World Cup XXI, July 2017.

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Abstract

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.

BibTeX

@InCollection{LNAI17-jmenashe,
  author = {Jacob Menashe and Josh Kelle and Katie Genter and Josiah Hanna and Elad Liebman and Sanmit Narvekar and Ruohan Zhang and Peter Stone},
  title = {Fast and Precise Black and White Ball Detection for RoboCup Soccer},
  booktitle = {{R}obo{C}up-2017: Robot Soccer World Cup {XXI}},
  location = {Nagoya, Japan},
  month = {July},
  year = {2017},
  abstract = {
  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.
  },
}

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