Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis.
G. Xu et al., "An HMM-Based Framework for Video Semantic Analysis," Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/TCSVT.2005.856903
Keywords and Phrases
Event Detection; HMM-Based Framework; Basketball Event Detection; Connectors; Detectors; Hidden Markov Model; Hidden Markov Models; Hidden Markov Models (HMMs); Image Classification; Image Motion Analysis; Image Representation; Image Sequences; Indexing; Motion Representation Scheme; Recognition; Soccer Shot Classification; Sport; Sports Video; Sports Videos; Training Process; Vector Source; Video Coding; Video Indexing; Video Semantic Analysis; Video Structuring; Volleyball Sequence Analysis
International Standard Serial Number (ISSN)
Article - Journal
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