Neurally Inspired Mechanisms for the Dynamic Visual Attention Map Generation Task

A model for dynamic visual attention is briefly introduced in this paper. A PSM (problem-solving method) for a generic “Dynamic Attention Map Generation” task to obtain a Dynamic Attention Map from a dynamic scene is proposed. Our approach enables tracking objects that keep attention in accordance with a set of characteristics defined by the observer. This paper mainly focuses on those subtasks of the model inspired in neuronal mechanisms, such as accumulative computation and lateral interaction. The subtasks which incorporate these biologically plausible capacities are called “Working Memory Generation” and “Thresholded Permanency Calculation”.