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Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video
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Source International Multimedia Conference archive
Proceedings of the eleventh ACM international conference on Multimedia table of contents
Berkeley, CA, USA
SESSION: Content analysis table of contents
Pages: 11 - 20  
Year of Publication: 2003
ISBN:1-58113-722-2
Authors
Xinguo Yu  Institute for Infocomm Research, Singapore
Changsheng Xu  Institute for Infocomm Research, Singapore
Hon Wai Leong  National University of Singapore, Singapore
Qi Tian  Institute for Infocomm Research, Singapore
Qing Tang  Institute for Infocomm Research, Singapore
Kong Wah Wan  Institute for Infocomm Research, Singapore
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 16,   Downloads (12 Months): 171,   Citation Count: 14
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ABSTRACT

This paper first presents an improved trajectory-based algorithm for automatically detecting and tracking the ball in broadcast soccer video. Unlike the object-based algorithms, our algorithm does not evaluate whether a sole object is a ball. Instead, it evaluates whether a candidate trajectory, which is generated from the candidate feature image by a candidate verification procedure based on Kalman filter,, which is generated from the candidate feature image by a candidate verification procedure based on Kalman filter, is a ball trajectory. Secondly, a new approach for automatically analyzing broadcast soccer video is proposed, which is based on the ball trajectory. The algorithms in this approach not only improve play-break analysis and high-level semantic event detection, but also detect the basic actions and analyze team ball possession, which may not be analyzed based only on the low-level feature. Moreover, experimental results show that our ball detection and tracking algorithm can achieve above 96% accuracy for the video segments with the soccer field. Compared with the existing methods, a higher accuracy is achieved on goal detection and play-break segmentation. To the best of our knowledge, we present the first solution in detecting the basic actions such as touching and passing, and analyzing the team ball possession in broadcast soccer video.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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A. Ekin, A. M. Tekalp. Robust dominant color region detection with applications to sports video, submitted to Comp. Vision & Image Understanding.
 
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A. Ekin, A.M. Tekalp, and R. Mehrotra. Automatic Soccer Video Analysis and Summarization, IEEE Trans. on Image Processing, Vol. 12:7(2003), 796--807.
 
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X. Yu, Q. Tian, and K. W. Wan. A novel ball detection framework for real soccer video, In Proc. ICME 2003, Vol II, 265--268.
 
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X. Yu, C. Xu, Q. Tian, and H. W. Leong. A ball tracking framework for broadcast soccer video, In Proc. ICME 2003, Vol II, 273--276.

CITED BY  14
 
 
 
 

Collaborative Colleagues:
Xinguo Yu: colleagues
Changsheng Xu: colleagues
Hon Wai Leong: colleagues
Qi Tian: colleagues
Qing Tang: colleagues
Kong Wah Wan: colleagues