An Adaptive Machine Director

We model the class of problem faced by a video broadcast director, who must act as an active perception agent to select a view of interest to a human from a range of possibilities. Real-time learning of a broadcast direction policy is achieved by efficient online Bayesian learning of the model’s parameters based on intermittent user feedback. In contrast to existing machine direction systems, which are dedicated to a particular scenario, our novel approach allows flexible learning of direction policies for novel domains or for viewerspecific preferences. We illustrate the flexibility of our approach by applying our model to a selection of scenarios with audio-visual input including teleconferencing, meetings and dance entertainment.