What Players do with the Ball: A Physically Constrained Interaction Modeling

Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in low-resolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players. In this paper, we propose a generic and principled approach to modeling the interaction between the ball and the players while also imposing appropriate physical constraints on the ball's trajectory. We show that our approach, formulated in terms of a Mixed Integer Program, is more robust and more accurate than several state-of-the-art approaches on real-life volleyball, basketball, and soccer sequences.

[1]  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.

[2]  Richard S. Zemel,et al.  Combining discriminative features to infer complex trajectories , 2006, ICML.

[3]  Charless C. Fowlkes,et al.  Globally-optimal greedy algorithms for tracking a variable number of objects , 2011, CVPR 2011.

[4]  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 .

[5]  Daniel Link,et al.  Tracking of Ball and Players in Beach Volleyball Videos , 2014, PloS one.

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

[7]  Hua-Tsung Chen,et al.  Physics-Based Ball Tracking in Volleyball Videos with its Applications to Set Type Recognition and Action Detection , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[8]  S. Savarese,et al.  Learning an Image-Based Motion Context for Multiple People Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[11]  Pascal Fua,et al.  Tracking Interacting Objects Using Intertwined Flows , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  Pascaline Parisot,et al.  Consensus-based trajectory estimation for ball detection in calibrated cameras systems , 2019, Journal of Real-Time Image Processing.

[14]  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.

[15]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, 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]  Hua-Tsung Chen,et al.  Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video , 2009, J. Vis. Commun. Image Represent..

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

[19]  Pascal Fua,et al.  Take your eyes off the ball: Improving ball-tracking by focusing on team play , 2014, Comput. Vis. Image Underst..

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

[21]  Pascal Fua,et al.  Tracking Interacting Objects Optimally Using Integer Programming , 2014, ECCV.

[22]  Yaser Sheikh,et al.  Representing and Discovering Adversarial Team Behaviors Using Player Roles , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[25]  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.

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

[27]  Christophe De Vleeschouwer,et al.  Distributed video acquisition and annotation for sport-event summarization , 2008 .

[28]  Wen Gao,et al.  Trajectory based event tactics analysis in broadcast sports video , 2007, ACM Multimedia.

[29]  Jing Zhang,et al.  Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[31]  Marti A. Hearst Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[32]  Sukadev Meher,et al.  A real-time trajectory-based ball detection-and-tracking framework for basketball video , 2013 .