AUTOMATIC EVOLUTION TRACKING FOR ARCHITECTURE TENNIS MATCHES USING AN HMM-BASED

Creating a cognitive vision system which will infer 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 evc- lution tracking of a tennis match using standard broadcast video sequences as input data. The use of a hierarchical structure consist- ing of Hidden Markov Models is proposed. This will take low-level events as its input and produce an output where the final state will indicate 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 perfor- mance degraded gracefully.