Dynamic Visual Attention: competitive versus motion priority scheme

Defined as attentive process in presence of visual sequences, dynamic visual attention responds to static and motion features as well. For a computer model, a straightforward way to integrate these features is to combine all features in a competitive scheme: the saliency map contains a contribution of each feature, static and motion. Another way of integration is to combine the features in a motion priority scheme: in presence of motion, the saliency map is computed as the motion map, and in absence of motion, as the static map. In this paper, four models are considered: two models based on a competitive scheme and two models based on a motion priority scheme. The models are evaluated experimentally by comparing them with respect to the eye movement patterns of human subjects, while viewing a set of video sequences. Qualitative and quantitative evaluations, performed in the context of simple synthetic video sequences, show the highest performance of the motion priority scheme, compared to the competitive scheme

Motion Integration in Visual Attention Models for Predicting Simple Dynamic Scenes

Visual attention models mimic the ability of a visual system, to detect potentially relevant parts of a scene. This process of attentional selection is a prerequisite for higher level tasks such as object recognition. Given the high relevance of temporal aspects in human visual attention, dynamic information as well as static information must be considered in computer models of visual attention. While some models have been proposed for extending to motion the classical static model, a comparison of the performances of models integrating motion in different manners is still not available. In this article, we present a comparative study of various visual attention models combining both static and dynamic features. The considered models are compared by measuring their respective performance with respect to the eye movement patterns of human subjects. Simple synthetic video sequences, containing static and moving objects, are used to assess the model suitability. Qualitative and quantitative results provide a ranking of the different model