Strategy diagram for identifying play strategies in multi-view soccer video data

Authors:
Yukihiro Nakamura;Shin Ando;Kenji Aoki;Hiroyuki Mano;Einoshin Suzuki
Affiliations:
Electrical and Computer Engineering, Yokohama National University, Japan;Electrical and Computer Engineering, Yokohama National University, Japan;Electrical and Computer Engineering, Yokohama National University, Japan;Electrical and Computer Engineering, Yokohama National University, Japan;Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Venue:
DS'06 Proceedings of the 9th international conference on Discovery Science
Year:
2006

Citing 7
Cited 0

Advanced Scout: Data Mining and Knowledge Discovery in NBA Data

Data Mining and Knowledge Discovery
Tracking Players and a Ball in Video Image Sequence and Estimating Camera Parameters for 3D Interpretation of Soccer Games

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Robust Tracking of Soccer Players Based on Data Fusion

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Structure analysis of soccer video with domain knowledge and hidden Markov models

Pattern Recognition Letters - Video computing
Finding interesting pass patterns from soccer game records

PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On the Automated Interpretation and Indexing of American Football

ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Automatic soccer video analysis and summarization

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a strategy diagram to acquire knowledge of soccer for identifying play strategies in multi-view video data. Soccer, as the most popular team sport in the world, attracts attention of researchers in knowledge discovery and data mining and its related areas. Domain knowledge is mandatory in such applications but acquiring domain knowledge of soccer from experts is a laborious task. Moreover such domain knowledge is typically acquired and used in an ad-hoc manner. Diagrams in textbooks can be considered as a promising source of knowledge and are intuitive to humans. Our strategy diagram enables a systematic acquisition and use of such diagrams as domain knowledge for identifying play strategies in video data of a soccer game taken from multiple angles. The key idea is to transform multi-view video data to sequential coordinates then match the strategy diagram in terms of essential features. Experiments using video data of a national tournament for high school students show that the proposed method exhibits promising results and gives insightful lessons for further studies.