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 viewer specific 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.