Take your eyes off the ball: Improving ball-tracking by focusing on team play

Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences.

[1]  Rama Chellappa,et al.  Learning multi-modal densities on Discriminative Temporal Interaction Manifold for group activity recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Tiziana D'Orazio,et al.  Real-time multiview analysis of soccer matches for understanding interactions between ball and players , 2008, CIVR '08.

[3]  Andrea Cavallaro,et al.  Detector-less ball localization using context and motion flow analysis , 2010, 2010 IEEE International Conference on Image Processing.

[4]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Stefan Carlsson,et al.  Stefan Carlsson Football Tracking in Wide-Screen Video Sequences , 2004 .

[6]  Rick Cavallaro,et al.  The FoxTrax Hockey Puck Tracking System , 1997, IEEE Computer Graphics and Applications.

[7]  Yoshiaki Shirai,et al.  Tracking players and estimation of the 3D position of a ball in soccer games , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[8]  Philippe C. Cattin,et al.  Tracking the invisible: Learning where the object might be , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Irfan A. Essa,et al.  Detecting regions of interest in dynamic scenes with camera motions , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Matej Kristan,et al.  A trajectory-based analysis of coordinated team activity in a basketball game , 2009, Comput. Vis. Image Underst..

[11]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[12]  Changsheng Xu,et al.  Collaborate ball and player trajectory extraction in broadcast soccer video , 2008, 2008 19th International Conference on Pattern Recognition.

[13]  James J. Little,et al.  Tracking and recognizing actions of multiple hockey players using the boosted particle filter , 2009, Image Vis. Comput..

[14]  Vincent Lepetit,et al.  Robust data association for online application , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  Pascal Fua,et al.  Multi-Commodity Network Flow for Tracking Multiple People , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Tiziana D'Orazio,et al.  A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[17]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[18]  Noel E. O'Connor,et al.  Team Activity Recognition in Sports , 2012, ECCV.

[19]  Greg Mori,et al.  Social roles in hierarchical models for human activity recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Larry S. Davis,et al.  Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Ming Xu,et al.  Strathprints Institutional Repository (2008) Real-time Modeling of 3-d Soccer Ball Trajectories from Multiple Fixed Cameras. Ieee Transactions on Circuits and Systems for Video Technology, 18 (3). Pp. 350-362. Issn 1051-8215 , 2022 .

[22]  Mubarak Shah,et al.  Motion and Appearance Contexts for Tracking and Re-Acquiring Targets in Aerial Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Robert J. Woodham,et al.  Video analysis of hockey play in selected game situations , 2009, Image Vis. Comput..

[24]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[25]  William J. Christmas,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Layered Data Association Using Graph-theoretic Formulation with Application to Tennis Ball Tracking in Monocular Sequences , 2022 .

[26]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Shimon Ullman,et al.  The chains model for detecting parts by their context , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Christophe De Vleeschouwer,et al.  Graph-based filtering of ballistic trajectory , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[29]  FuaPascal,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008 .

[30]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[31]  David R. Bull,et al.  Projective image restoration using sparsity regularization , 2013, 2013 IEEE International Conference on Image Processing.