TY - GEN
T1 - Techniques for Automatic Video Content Derivation
AU - Petkovic, M.
AU - Mihajlovic, V.
AU - Jonker, Willem
N1 - Imported from EWI/DB PMS [db-utwente:inpr:0000003595]
PY - 2003/9
Y1 - 2003/9
N2 - 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.
AB - 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.
KW - DB-MMR: MULTIMEDIA RETRIEVAL
KW - METIS-216365
KW - IR-46872
KW - EWI-7308
U2 - 10.1109/ICIP.2003.1246754
DO - 10.1109/ICIP.2003.1246754
M3 - Conference contribution
SN - 0-7803-7750-8
T3 - IEEE Conference Proceedings
SP - 611
EP - 614
BT - Proceedings of the International Conference on Image Processing (ICIP 2003)
PB - IEEE Computer Society
CY - Los Alamitos
ER -