Title :
Techniques for automatic video content derivation
Author :
Petkovic, Milan ; Mihajlovic, Vojkan ; Jonker, Willem
Author_Institution :
Twente Univ., Enschede, Netherlands
Abstract :
In this paper, we focus on the use of three different techniques that support automatic derivation of video content from raw video data, namely, a spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. These techniques are validated in the particular domain of tennis and Formula 1 race videos. We present the experimental results for the detection of events such as net-playing, rally, service, and forehand stroke among others in the Tennis domain, as well as excited speech, start, fly-out, passing, and highlights in the Formula 1 domain.
Keywords :
belief networks; content-based retrieval; hidden Markov models; image retrieval; spatiotemporal phenomena; video signal processing; Formula 1 race video; automatic video content derivation technique; content-based video retrieval; dynamic Bayesian network; event detection; forehand stroke event; hidden Markov model; net-playing event; rally event; raw video data; spatio-temporal rule-based method; tennis domain; video processing; Bayesian methods; Computer aided software engineering; Content based retrieval; Data mining; Event detection; Hidden Markov models; Information retrieval; Shape; Speech; TV broadcasting;
Conference_Titel :
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on
Print_ISBN :
0-7803-7750-8
DOI :
10.1109/ICIP.2003.1246754
