Sports highlight detection from keyword sequences using HMM

Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the tempo...

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Bibliographic Details
Main Authors: Jinjun Wang, Changsheng Xu, Engsiong Chng, Qi Tian
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bridges
Computer science
control theory
systems
Data processing. List processing. Character string processing
Event detection
Exact sciences and technology
Games
Hidden Markov models
High definition video
Humans
Memory organisation. Data processing
Motion pictures
Multimedia communication
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Software
Speech
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Summary:Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of keyword sequences is analyzed by an HMM classifier. The creation of visual and audio keywords can help to bridge the gap between low-level features and high-level semantics. The use of the HMM classifier can automatically find the temporal change character of the event instead of rule based heuristic modeling to map certain keyword sequences into events. Experiments using our model on soccer games produced some promising results
DOI:10.1109/ICME.2004.1394263