Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project.
Semantic annotation of soccer videos: automatic highlights identification / ASSFALG J.; BERTINI M.; C. COLOMBO; DEL BIMBO A.; NUNZIATI W.. - In: COMPUTER VISION AND IMAGE UNDERSTANDING. - ISSN 1077-3142. - STAMPA. - 92(2/3):(2003), pp. 285-305. [doi:10.1016/j.cviu.2003.06.004]
Semantic annotation of soccer videos: automatic highlights identification
BERTINI, MARCO;COLOMBO, CARLO;DEL BIMBO, ALBERTO;
2003
Abstract
Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.