Depth in the visual attention modelling from the egocentric perspective of view

Abstract

An extensive research has been held in the field of the visual attention modelling throughout the past years. However, the egocentric visual attention in real environments has still not been thoroughly studied. We introduce a method proposal for conducting automated user studies on the egocentric visual attention in a laboratory. Goal of our method is to study distance of the objects from the observer (their depth) and its influence on the egocentric visual attention. The user studies based on the method proposal were conducted on a sample of 37 participants and our own egocentric dataset was created. The whole experimental and evaluation process was designed and realized using advanced methods of computer vision. Results of our research are ground-truth values of the egocentric visual attention and their relation to the depth of the scene approximated as a depth-weighting saliency function. The depth-weighting function was applied on the state-of-the-art models and evaluated. Our enhanced models provided better results than the current depthweighting saliency models.