poster

Tracking for context extraction in athletic events

Authors:

Aristodemos Pnevmatikakis,

Francesco TobiaAuthors Info & Claims

SAPMIA '10: Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access

Pages 67 - 72

https://doi.org/10.1145/1878061.1878081

Published: 29 October 2010 Publication History

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Abstract

Personalisation of large scale athletic events requires camera-specific annotations to provide for reasoning about incidents being best viewed by specific cameras. This needs an automatic system for annotating athletes on the video streams, to be achieved by the use of person trackers. In this paper we present a novel approach towards combining scene segmentation, motion outliers, face and bib tracks into body hypotheses and tracking them across time. Preliminary evaluation results demonstrate the potential of the proposed approach.

References

[1]

J. Bouguet. Pyramidal implementation of the lucas-kanade feature tracker: description of the algorithm. Technical report, OpenCV Document, Intel Microprocessor Research Labs, 2000.

Google Scholar

[2]

G. Bradski, A. Kaehler, and V. Pisarevsky. Learning-based computer vision with intel's open source computer vision library. Intel Technology Journal, 9, 2005.

Google Scholar

[3]

P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. Int. J. Comput. Vision, 59(2):167--181, 2004.

Digital Library

Google Scholar

[4]

S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6):721--741, November 1984.

Digital Library

Google Scholar

[5]

C. Harris and M. Stephens. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, pages 147--151, 1988.

Crossref

Google Scholar

[6]

G. Jaffré and A. Crouzil. Non-rigid object localization from color model using mean shift. In IEEE International Conference on Image Processing (ICIP 2003), pages 317--320, Barcelona, Spain, September 2003.

Crossref

Google Scholar

[7]

K. Jung, K. I. Kim, and A. K. Jain. Text information extraction in images and video: a survey. Pattern Recognition, 37(5):977--997, 2004.

Crossref

Google Scholar

[8]

N. Katsarakis and A. Pnevmatikakis. Event detection in athletics for personalized sports content delivery. In International Workshop on Image Analysis for Multimedia Interactive Services, pages 280--283, London, UK, May 2009.

Crossref

Google Scholar

[9]

B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In 7th International Joint Conference on Artificial Intelligence (IJCAI '81), pages 674--679, Vancouver, BC, Canada, August 1981.

Digital Library

Google Scholar

[10]

S. Messelodi and C. Modena. Automatic identification and skew estimation of text lines in real scene images. Pattern Recognition, 32(5):791--810, May 1999.

Crossref

Google Scholar

[11]

C. M. Modena. Segmentation of sport videos by embedded text reading. FBK-irst Technical Report no. 5508, February 2010.

Google Scholar

[12]

P. A. Viola and M. J. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pages 511--518, Kauai, HI, USA, December 2001.

Crossref

Google Scholar

Cited By

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Shih H(2018)A Survey of Content-Aware Video Analysis for SportsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.265562428:5(1212-1231)Online publication date: May-2018
https://doi.org/10.1109/TCSVT.2017.2655624
Messelodi SModena C(2018)Scene text recognition and tracking to identify athletes in sport videosMultimedia Tools and Applications10.1007/s11042-011-0878-y63:2(521-545)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s11042-011-0878-y
Barhoumi W(2014)Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequencesSignal, Image and Video Processing10.1007/s11760-014-0630-y9:7(1705-1715)Online publication date: 15-Mar-2014
https://doi.org/10.1007/s11760-014-0630-y

Index Terms

Tracking for context extraction in athletic events
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Information & Contributors

Information

Published In

SAPMIA '10: Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access

October 2010

86 pages

ISBN:9781450301718

DOI:10.1145/1878061

General Chairs:
David Vallet
Universidad Autónoma de Madrid, Spain
,
Naeem Ramzan
Queen Mary, University London, UK
,
Martin Halvey
University of Glasgow, UK
,
Charalampos Z. Patrikakis
National Tech.University of Athens, Greece

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2010

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SIGMM

MM '10: ACM Multimedia Conference

October 29, 2010

Firenze, Italy

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Cited By

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Shih H(2018)A Survey of Content-Aware Video Analysis for SportsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.265562428:5(1212-1231)Online publication date: May-2018
https://doi.org/10.1109/TCSVT.2017.2655624
Messelodi SModena C(2018)Scene text recognition and tracking to identify athletes in sport videosMultimedia Tools and Applications10.1007/s11042-011-0878-y63:2(521-545)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s11042-011-0878-y
Barhoumi W(2014)Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequencesSignal, Image and Video Processing10.1007/s11760-014-0630-y9:7(1705-1715)Online publication date: 15-Mar-2014
https://doi.org/10.1007/s11760-014-0630-y

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