Automatic detection of regions of interest in complex video sequences

Studies of visual attention and eye movements have shown that people generally attend to only a few areas in typical scenes. These areas are commonly referred to as regions of interest (ROIs). When scenes are viewed with the same context and motivation, these ROIs are often highly correlated amongst different people, motivating the development of computational models of visual attention. This paper describes a novel model of visual attention designed to provide an accurate and robust prediction of a viewer's locus of attention across a wide range of typical video content. The model has been calibrated and verified using data gathered in an experiment in which the eye movements of 24 viewers were recorded while viewing material form a large database of still and video scenes. Certain characteristics of the scene content, such as moving objects, people, foreground and centrally-located objects, were found to exert a strong influence on viewers' attention. The results of comparing model predictions to experimental data demonstrate a strong correlation between the predicted ROIs and viewers' fixations.