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Paper in CVIU 2013 “A Visualization Framework for Team Sports Captured using Multiple Static Cameras”

October 3rd, 2013 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Jessica Hodgins, PAMI/ICCV/CVPR/ECCV, Papers, Raffay Hamid, Sports Visualization No Comments »

  • R. Hamid, R. Kumar, J. Hodgins, and I. Essa (2013), “A Visualization Framework for Team Sports Captured using Multiple Static Cameras,” Computer Vision and Image Understanding, p. -, 2013. [PDF] [WEBSITE] [VIDEO] [DOI] [BIBTEX]
    @article{2013-Hamid-VFTSCUMSC,
      Author = {Raffay Hamid and Ramkrishan Kumar and Jessica Hodgins and Irfan Essa},
      Date-Added = {2013-10-22 13:42:46 +0000},
      Date-Modified = {2014-04-28 17:09:21 +0000},
      Doi = {10.1016/j.cviu.2013.09.006},
      Issn = {1077-3142},
      Journal = {{Computer Vision and Image Understanding}},
      Number = {0},
      Pages = {-},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Hamid-VFTSCUMSC.pdf},
      Title = {A Visualization Framework for Team Sports Captured using Multiple Static Cameras},
      Url = {http://raffayhamid.com/sports_viz.shtml},
      Video = {http://www.youtube.com/watch?v=VwzAMi9pUDQ},
      Year = {2013},
      Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S1077314213001768},
      Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.cviu.2013.09.006},
      Bdsk-Url-3 = {http://raffayhamid.com/sports_viz.shtml}}

Abstract

We present a novel approach for robust localization of multiple people observed using a set of static cameras. We use this location information to generate a visualization of the virtual offside line in soccer games. To compute the position of the offside line, we need to localize players′ positions, and identify their team roles. We solve the problem of fusing corresponding players′ positional information by finding minimum weight K-length cycles in a complete K-partite graph. Each partite of the graph corresponds to one of the K cameras, whereas each node of a partite encodes the position and appearance of a player observed from a particular camera. To find the minimum weight cycles in this graph, we use a dynamic programming based approach that varies over a continuum from maximally to minimally greedy in terms of the number of graph-paths explored at each iteration. We present proofs for the efficiency and performance bounds of our algorithms. Finally, we demonstrate the robustness of our framework by testing it on 82,000 frames of soccer footage captured over eight different illumination conditions, play types, and team attire. Our framework runs in near-real time, and processes video from 3 full HD cameras in about 0.4 seconds for each set of corresponding 3 frames.

via Science Direct A Visualization Framework for Team Sports Captured using Multiple Static Cameras.

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Paper in CVPR (2010): “Motion Field to Predict Play Evolution in Dynamic Sport Scenes

June 13th, 2010 Irfan Essa Posted in Activity Recognition, Jessica Hodgins, Kihwan Kim, Matthias Grundmann, PAMI/ICCV/CVPR/ECCV, Papers, Sports Visualization No Comments »

Kihwan Kim, Matthias Grundmann, Ariel Shamir, Iain Matthews, Jessica Hodgins, Irfan Essa (2010) “Motion Field to Predict Play Evolution in Dynamic Sport Scenes” in Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), San Francisco, CA, USA, June 2010 [PDF][Website][DOI][Video (Youtube)].

Abstract

Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game. We show that constrained multi-agent events can be analyzed and even predicted from video. Such analysis requires estimating the global movements of all players in the scene at any time, and is needed for modeling and predicting how the multi-agent play evolves over time on the field. To this end, we propose a novel approach to detect the locations of where the play evolution will proceed, e.g. where interesting events will occur, by tracking player positions and movements over time. We start by extracting the ground level sparse movement of players in each time-step, and then generate a dense motion field. Using this field we detect locations where the motion converges, implying positions towards which the play is evolving. We evaluate our approach by analyzing videos of a variety of complex soccer plays.

CVPR 2010 Paper on Play Evolution

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Paper in CVPR (2010): “Player Localization Using Multiple Static Cameras for Sports Visualization”

June 13th, 2010 Irfan Essa Posted in Activity Recognition, Jessica Hodgins, Kihwan Kim, Matthias Grundmann, Numerical Machine Learning, PAMI/ICCV/CVPR/ECCV, Raffay Hamid, Sports Visualization No Comments »

Raffay Hamid, Ram Krishan Kumar, Matthias Grundmann, Kihwan Kim, Irfan Essa, Jessica Hodgins (2010), “Player Localization Using Multiple Static Cameras for Sports Visualization” In Proceedings of IEEE Computer Vision and Pattern Recognition Conference (CVPR), San Francisco, CA, USA, June 2010 [PDF][Website][DOI][Video (Youtube)].

Abstract

We present a novel approach for robust localization of multiple people observed using multiple cameras. We usethis location information to generate sports visualizations,which include displaying a virtual offside line in soccer games, and showing players’ positions and motion patterns.Our main contribution is the modeling and analysis for the problem of fusing corresponding players’ positional informationas finding minimum weight K-length cycles in complete K-partite graphs. To this end, we use a dynamic programmingbased approach that varies over a continuum of being maximally to minimally greedy in terms of the numberof paths explored at each iteration. We present an end-to-end sports visualization framework that employs our proposed algorithm-class. We demonstrate the robustness of our framework by testing it on 60; 000 frames of soccerfootage captured over 5 different illumination conditions, play types, and team attire.

Teaser Image from CVPR 2010 paper

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CVPR 2010: Accepted Papers

April 1st, 2010 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Jessica Hodgins, Kihwan Kim, Matthias Grundmann, PAMI/ICCV/CVPR/ECCV, Papers, Vivek Kwatra No Comments »

We have the following 4 papers that have been accepted for publications in IEEE CVPR 2010. More details forthcoming, with links to more details.
  • Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan Essa (2010) “Discontinuous Seam-Carving for Video Retargeting” (a GA Tech, Google Collaboration)
  • Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan Essa (2010) “Efficient Hierarchical Graph-Based Video Segmentation” (a GA Tech, Google Collaboration)
  • Kihwan Kim, Matthias Grundmann, Ariel Shamir, Iain Matthews, Jessica Hodgins, and Irfan Essa (2010) “Motion Fields to Predict Play Evolution in Dynamic Sport Scenes” (a GA Tech, Disney Collaboration)
  • Raffay Hamid, Ramkrishan Kumar, Matthias Grundmann, Kihwan Kim, Irfan Essa, and Jessica Hodgins (2010) “Player Localization Using Multiple Static Cameras for Sports Visualization” (a GA Tech, Disney Collaboration)
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Disney Research, Pittsburgh

October 23rd, 2008 Irfan Essa Posted in Jessica Hodgins No Comments »

This academic year, I am spending some time working with the newly formed Disney Research, Pittsburgh, (Directed by Jessica Hodgins) formed next to CMU.  The press release is announcing this lab is here (Carnegie Mellon SCS Press Release). I am also hanging out with folks at the CMU Robotics Institute and have started some new collaborations.  So now depending on when, you can find me either in Atlanta (at GA Tech) or in Pittsburgh (at Disney Lab or CMU) [OR on a airplane between Pittsburgh and Atlanta].

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