AFL Player Detection and Tracking

This paper is an empirical study of the application of visual detection and tracking methods to the problem of locating and tracking all AFL players during a game. While most person detection and tracking algorithms are designed for pedestrians, we show that with appropriate modifications, state of the art methods can be adapted to a more challenging domain where motion is significantly more varied and occurs in a much wider area.

[1]  Konrad Schindler,et al.  Continuous Energy Minimization for Multitarget Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Yanxi Liu,et al.  Tracking Sports Players with Context-Conditioned Motion Models , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Irfan A. Essa,et al.  Player localization using multiple static cameras for sports visualization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Kai Ming Ting,et al.  Precision and Recall , 2017, Encyclopedia of Machine Learning and Data Mining.

[6]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Luc Van Gool,et al.  Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[9]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[10]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  James J. Little,et al.  Self-Learning for Player Localization in Sports Video , 2013, ArXiv.

[14]  Konrad Schindler,et al.  Discrete-continuous optimization for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[16]  Stefan Carlsson,et al.  Multi-Target Tracking - Linking Identities using Bayesian Network Inference , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Yuqian Wu,et al.  Multi-person tracking-by-detection with local particle filtering and global occlusion handling , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[18]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Irfan A. Essa,et al.  A visualization framework for team sports captured using multiple static cameras , 2014, Comput. Vis. Image Underst..

[21]  Rainer Stiefelhagen,et al.  The CLEAR 2006 Evaluation , 2006, CLEAR.

[22]  A. Ellis,et al.  PETS2009 and Winter-PETS 2009 results: A combined evaluation , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

[23]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[24]  Konrad Schindler,et al.  Multi-Target Tracking by Discrete-Continuous Energy Minimization , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.