Automatic production of personalized basketball video summaries from multi-sensored data

We propose a flexible framework for producing highly personalized basketball video summaries, by integrating contextural information, narrative user preferences on story pattern, and general production principles. Starting from the multiple streams captured by a distributed set of fixed cameras, we study the implementation of autonomous viewpoint determination and automatic temporal segment selection, and also discuss the production of visually comfortable output, by applying smoothing process to viewpoint selection and by defining efficient benefit functions to evaluate various summary organization. The efficiency of our framework is demonstrated by experimental results.