Automatic evolution tracking for tennis matches using an HMM-based architecture

Creating a cognitive vision system which infers high-level semantic information from low-level feature and event information for a given type of multimedia content is a problem attracting many researchers´ attention in recent years. In this work, we address the problem of automatic interpretation and evolution tracking of a tennis match using standard broadcast video sequences as input data. The use of a hierarchical structure consisting of hidden Markov models is proposed. This takes low-level events as its input and produce an output where the final state indicates if the point is to be awarded to one player or another. Using ground-truth data as input for the classifier described, the points are always correctly awarded to the players. Even when modifying the ground-truth data with errors randomly inserted in it and use it as input for the proposed system, the system performance degraded gracefully