Event recognition and classification in sports video using HMM

Sports event recognition and classification is a challenging task due to the number of possible categories. On one hand, how to characterise legitimate occasion classification names and how to acquire preparing tests for these classes should be investigated; then again, it is non-inconsequential to accomplish acceptable order execution. To address these issues, 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 modelling the evaluation of the position of the object with a hidden Markov model (HMM). Snooker is used as an example for this purpose of 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. The temporal behaviour of the white ball is modelled using a HMM where each model is representative of a particular semantic event.