Title:
A Visualization Framework for Team Sports Captured using Multiple Static Cameras
A Visualization Framework for Team Sports Captured using Multiple Static Cameras
| dc.contributor.author | Hamid, Raffay | |
| dc.contributor.author | Kumar, Ramkrishan | |
| dc.contributor.author | Hodgins, Jessica K. | |
| dc.contributor.author | Essa, Irfan | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Disney Research | en_US |
| dc.date.accessioned | 2014-03-13T17:52:10Z | |
| dc.date.available | 2014-03-13T17:52:10Z | |
| dc.date.issued | 2013 | |
| dc.description | This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. | en_US |
| dc.description | DOI: http://dx.doi.org/ 10.1016/j.cviu.2013.09.006 | |
| dc.description.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. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | R. Hamid, R. Kumar, J. Hodgins, and I. Essa (2013), “A Visualization Framework for Team Sports Captured using Multiple Static Cameras,” To appear in Computer Vision and Image Understanding, 2013. | en_US |
| dc.identifier.doi | 10.1016/j.cviu.2013.09.006 | |
| dc.identifier.uri | http://hdl.handle.net/1853/51325 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Computer vision | en_US |
| dc.subject | Data fusion | en_US |
| dc.subject | Multi-camera tracking | en_US |
| dc.subject | Perceptual reasoning | en_US |
| dc.subject | Sports visualization | en_US |
| dc.subject | Video analysis | en_US |
| dc.title | A Visualization Framework for Team Sports Captured using Multiple Static Cameras | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dc.type.genre | Post-print | |
| dspace.entity.type | Publication | |
| local.contributor.author | Essa, Irfan | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | College of Computing | |
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