A digital on-demand video service supporting content-based queries
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
An approach for video meta-data modeling and query processing
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Distributed Multimedia QoS Parameters from Presentation Modelling by Coloured Petri Nets
MHVR '94 Selected papers from the First International Conference on Hypermedia, Multimedia, and Virtual Reality: Models, Systems, and Applications
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Video Semantic Content Analysis Framework Based on Ontology Combined MPEG-7
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
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Digital video networks are making available increasing amounts of sports video data. The volume of material on offer means that sports fans often rely on prepared summaries of game highlights to follow the progress of their favourite teams. A significant application area for automated video analysis technology is the generation of personalized highlights of sports events. One of the most popular sports around world is soccer. A soccer game is composed of a range of significant events, such as goal scoring, fouls, and substitutions. Automatically detecting these events in a soccer video can enable users to interactively design their own highlights programmes. From an analysis of broadcast soccer video, we propose a query description model based on Basic Semantic Unit Composite Petri-Nets (BSUCPN) to automatically detect significant events within soccer video. Firstly we define a Basic Semantic Unit (BSU) set for soccer videos based on identifiable feature elements within a soccer video, Secondly we design Composite Petri-Net (CPN) models for semantic queries and use these to describe BSUCPNs for semantic events in soccer videos. A particular strength of this approach is that users are able to design their own semantic event queries based on BSUCPNs to search interactively within soccer videos. Experimental results based on recorded soccer broadcasts are used to illustrate the potential of this approach