Effective feature extraction for play detection in American football video

Tie-Yan Liu, Wei-Ying Ma, Hong-Jiang Zhang

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

19 Citations (Scopus)

Abstract

The fact that a typical broadcast can last over 3 hours for a game of 60 minutes makes video summarization of American football games most desirable. In this paper, we present several feature extraction methods for play detection in American football video. Wavelet based motion analysis is used to extract the trend component from the noisy motion vectors; a hybrid field-color model detects field area with both high accuracy and fast speed; and a prior knowledge driven line detection method uses the court information to estimate miss-detections. Based on the so-extracted features, a boosting chain is used for feature selection and decision making. Tested on large-size video data, the detection performance of our work is very promising. © 2005 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 11th International Multimedia Modelling Conference, MMM 2005
Pages164-171
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event11th International Multimedia Modelling Conference, MMM 2005 - Melbourne, VIC, Australia
Duration: 12 Jan 200514 Jan 2005

Publication series

NameProceedings of the 11th International Multimedia Modelling Conference, MMM 2005

Conference

Conference11th International Multimedia Modelling Conference, MMM 2005
PlaceAustralia
CityMelbourne, VIC
Period12/01/0514/01/05

Bibliographical note

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