Modeling high level structure in sports with motion driven HMMs
In this paper, we investigate the retrieval of dynamic events that occur in broadcast sports footage. Dynamic events in sports are important in so far as they are related to the game semantics. Thus far, the temporal interleaving of camera views has been used to infer these types of events. We propose the use of the spatio-temporal behaviour of an object in the footage as an embodiment of a semantic event. This is accomplished by modeling the evolution of the position of the object with a hidden Markov model (HMM). Snooker is used as an example for the purpose of this research. The system firstly parses the video sequence based on the geometry of the content in the camera view and classifies the footage as a particular view type. Secondly, we consider the relative position of the white ball on the snooker table over the duration of a clip to embody semantic events. A colour based particle filter is employed to robustly track the snooker balls. The temporal behaviour of the white ball is modeled using a HMM where each model is representative of a particular semantic episode. Upon collision of the white ball with another coloured ball, a separate track is instantiated.