Using camera motion to identify types of American football plays

Authors:
M. Lazarescu;S. Venkatesh
Affiliations:
Sch. of Comput., Curtin Univ. of Technol., Perth, WA, Australia;Sch. of Comput., Curtin Univ. of Technol., Perth, WA, Australia
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Year:
2003

Citing 0
Cited 5

A fusion architecture based on TBM for camera motion classification

Image and Vision Computing
Event detection in sports video based on generative-discriminative models

EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Taxonomy of directing semantics for film shot classification

IEEE Transactions on Circuits and Systems for Video Technology
Recognizing Interactive Group Activities Using Temporal Interaction Matrices and Their Riemannian Statistics

International Journal of Computer Vision
Multi-agent event recognition by preservation of spatiotemporal relationships between probabilistic models

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method that uses camera motion parameters to recognise 7 types of American football plays. The approach is based on the motion information extracted from the video and it can identify short and long pass plays, short and long running plays, quarterback sacks, punt plays and kickoff plays. This method has the advantage that it is fast and it does not require player or ball tracking. The system was trained and tested using 782 plays and the results show that the system has an overall classification accuracy of 68%.