Abstract A five layered, event driven hierarchical framework for generic sports video classification has been proposed in this paper. The top layer classifications are based on a few popular audio and video content analysis techniques like short-time energy and Zero Crossing Rate (ZCR) for audio and Hidden Markov Model (HMM) based techniques for video, using color and motion as features. The lower layer classifications are done by applying game specific rules to recognize major events of the game. The proposed framework has been successfully tested with cricket and football video sequences. The event-related classifications bring us a step closer to the ultimate goal of semantic classifications that would be ideally required for sports highlight generation.
A Hierarchical Framework for Generic Sports Video Classification
Computer Vision – ACCV 2006 ; 7 ; 633-642
Lecture Notes in Computer Science ; 3852 , 7
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Hide Markov Model , Video Sequence , News Video , Video Shot , Sport Video Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
A Hierarchical Framework for Generic Sports Video Classification
British Library Conference Proceedings | 2006
|Statistical Framework for Shot Segmentation and Classification in Sports Video
Springer Verlag | 2007
|Hierarchical Decision Making Scheme for Sports Video Categorisation with Temporal Post-Processing
British Library Conference Proceedings | 2004
|