Confidence Based updation of Motion Conspicuity in Dynamic Scenes

Abstract

Computational models of visual attention result in considerable data compression by eliminating processing on regions likely to be devoid of meaningful content. While saliency maps in static images is indexed on image region (pixels), psychovisual data indicates that in dynamic scenes human attention is object driven and localized motion is a significant determiner of object conspicuity. We have introduced a confidence map, which indicates the uncertainty in the position of the moving objects incorporating the exponential loss of information as we move away from the fovea. We improve the model further using a computational model of visual attention based on perceptual grouping of objects with motion and computation of a motion saliency map based on localized motion conspicuity of the objects. Behaviors exhibited in the system include attentive focus on moving wholes, shifting focus in multiple object motion, focus on objects moving contrary to the majority motion. We also present experimental data contrasting the model with human gaze tracking in a simple visual task.