Gaze location prediction for broadcast football video using Bayesian integration of low level features and top-down cues

Accurate prediction of the viewer´s gaze location has the potential to improve bit allocation, rate control, error resilience and quality evaluation in video compression. With complex contexts, such as that of broadcast football video, the potential reward is even higher given that compression and transmission of this type of content is challenging. In this paper we propose a gaze location prediction system for high definition broadcast football video. The proposed system employs Bayesian integration of bottom-up features and context specific top-down cues. Our results show that the proposed model has better gaze prediction performance than other top-down models that we adapted to this context.