@techreport{TR-IC-PFG-17-06,
   number = {IC-PFG-17-06},
   author = {Gabriel Militão {Vinhas Lopes} and Esther Colombini},
   title  =  {{RoboCup Soccer Ball Depth Detection using Convolutional
                   Neural Networks}},
   month = {July},
   year = {2017},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 08 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       The  RoboCup, a soccer competition played by autonomous robots,
       raises  a  variety of hard problems in different fields such as
       Artificial  Intelligence and Dynamics. In particular, vision is
       the  essential  input  to  take  actions  based  on this highly
       dynamic  environment.  Although traditional techniques might be
       cheaper  for  object  detection, new rules such as multicolored
       balls  require  more  robust  detection  methods  and  allow  a 
       significant  computing  power  improvement.  Given these recent
       changes,  this  work  presents a solution for ball localization
       using  convolutional  neural networks (CNNs), a largely applied
       machine  learning  technique  holding  the  state  of  the  art 
       technology  for several imagery problems. To perform the object
       detection, we train the CNN with robot's camera images as input
       and the ball's coordinates relative to the robot as output.
  }
}