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We study sports video mining as a machine learning and statistical inference problem. We focus on mid-level semantic structures that can serve as building blocks for high-level semantic analysis. Particularly, we are interested in how to infer multiple coexistent structures jointly. We present a new multichannel segmental hidden Markov model (MCSHMM) that is a unique probabilistic graphical model...
We present a two-layer generative model for sport video mining that is composed of a two-layer observation model. The first layer is the Gaussian mixture model (GMM) using frame-wise camera motion for intra-shot analysis and the second layer is the hidden Markov model (HMM) involving the GMM as the mid-level observation for inter-shot analysis. A recursive model estimation method is developed for...
We present a generative model approach to explore intrinsic semantic structures in sport videos, e.g., the camera view in American football games. We will invoke the concept of semantic space to explicitly define the semantic structure in the video in terms of latent states. A dynamic model is used to govern the transition between states, and an observation model is developed to characterize visual...
We present a top-down statistical modeling approach to explore the semantic structure in American football video. First, a semantic space is defined where the video semantic structure is characterized by semantic units, a dynamic model over semantic units, and an observation model for mapping the semantic units with the visual features. Then, a new hidden Markov model (HMM)-based video generative...
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