event detection in sports video based on generative-discriminative models
We study event detection in the context of sports video mining that involves a three-layer semantic space, i.e., low-level visual features, mid-level semantic structures, and high-level semantics (or events). This space supports explicit semantic modeling and direct semantic computing. Specifically, the mid-level semantic structures are the basic recurrent temporal patterns that serve as the building blocks for event analysis. We also propose a unified video mining framework where event detection is formulated as two inter-related inference problems associated with two different machine learning tools. One is from low-level to mid-level by generative models, and the other is from mid-level to high-level by discriminate models. We use American football video analysis as a case study, and the experimental results demonstrate the promising results of the proposed approach.
event detection discriminative models video mining generative models sports video analysis