Learning a visual attention model for adaptive fast-forward in video surveillance

Abstract

The focus of visual attention is guided by salient signals in the peripheral field of view (bottom-up) as well as by the relevance feedback of a semantic model (top-down). As a result, humans are able to evaluate new situations very fast, with only a view numbers of fixations. In this paper, we present a learned model for the fast prediction of visual attention in video. We consider bottom-up and memory-less top-down mechanisms of visual attention guidance, and apply the model to video playback-speed adaption. The presented visual attention model is based on rectangle features that are fast to compute and capable of describing the known mechanisms of bottom-up processing, such as motion, contrast, color, symmetry, and others as well as topdown cues, such as face and person detectors. We show that the visual attention model outperforms other recent methods in adaption of video playback-speed.