Probabilistic Graphical Models for Sports Video Mining

Abstract

Semantic analysis is an active and interesting research topic in the field of sports video mining. In this chapter, the authors present a multi-level video semantic analysis framework that is featured by hybrid generative-discriminative probabilistic graphical models. A three-layer semantic space is proposed, by which the semantic video analysis is cast into two inter-related inference problems defined at different semantic levels. In the first stage, a multi-channel segmental hidden Markov model (MCSHMM) is developed to jointly detect multiple co-existent mid-level keywords from low-level visual features, which can serve as building blocks for high-level semantics. In the second stage, authors propose the auxiliary segmentation conditional random fields (ASCRFs) to discover the game flow from multi-channel key-words, which provides a unified semantic representation for both event and structure analysis. The use of hybrid generative-discriminative approaches in two different stages is proved to be effective and appropriate for multi-level semantic analysis in sports video. The experimental results from a set of American football video data demonstrate that the proposed framework offers superior results compared with other traditional machine learning-based video mining approaches.