Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Semantic Annotation of Sports Videos
IEEE MultiMedia
Color Set Size Problem with Application to String Matching
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Detection of slow-motion replay segments in sports video for highlights generation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Audio-based event detection for sports video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Content-based movie analysis and indexing based on audiovisual cues
IEEE Transactions on Circuits and Systems for Video Technology
Attention guided football video content recommendation on mobile devices
MobiMedia '06 Proceedings of the 2nd international conference on Mobile multimedia communications
Play segmentation for the play---break based sports video using a local adaptive model
Multimedia Tools and Applications
Story unit segmentation with friendly acoustic perception
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Content-based organisation, analysis and retrieval of soccer video
International Journal of Computer Applications in Technology
Football player detection in video broadcast
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
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We present a statistical approach for parsing football video structures. Based on video production conventions, a new generic structure called ‘attack' is identified, which is an equivalent of scene in other video domains. We define four video segments to construct it, namely play, focus, replay and break. Two middle level visual features, play field ratio and zoom size, are also computed. The detection process includes a two-pass classifier, a combination of Gaussian Mixture Model and Hidden Markov Models. A general suffix tree is introduced to identify and organize ‘attack'. In experiments, video structure classification accuracy of about 86% is achieved on broadcasting World Cup 2002 video data.