DocumentCode :
2958841
Title :
Shot and Scoring Events Identification of Basketball Videos
Author :
Huang, Chung-Lin ; Shih, Huang-Chia ; Chen, Ching-Lun
Author_Institution :
Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu
fYear :
2006
fDate :
9-12 July 2006
Firstpage :
1885
Lastpage :
1888
Abstract :
This paper presents a novel semantic-oriented video analysis system for the basketball game videos. Based on Bayesian belief network (BBN), it may bridge this gap between the low-level features describing image/video structure and the high-level knowledge. We apply the support vector machine (SVM) to identify and track the ball, the shooter, and the basket as the low-level features. Based on these features, our BBN framework can identify four categories of shot event such as short shot, medium shot, long shot, free throw, and the scoring event. In the experiments, we demonstrate that our system may interpret the video shots in terms of four different shot events and one scoring event effectively
Keywords :
belief networks; feature extraction; sport; support vector machines; tracking; video signal processing; BBN framework; Bayesian belief network; SVM; ball tracking; basketball video shot identification system; feature extraction; scoring event identification; semantic-oriented video analysis system; support vector machine; Bayesian methods; Bridges; Event detection; Feature extraction; Games; Hidden Markov models; Object detection; Support vector machine classification; Support vector machines; Videos;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo, 2006 IEEE International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
1-4244-0366-7
Electronic_ISBN :
1-4244-0367-7
Type :
conf
DOI :
10.1109/ICME.2006.262923
Filename :
4036992
Link To Document :
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