A Bottom-Up Spatiotemporal Visual Attention Model for Video Analysis

A video analysis framework based on spatiotemporal saliency calculation is presented. We propose a novel scheme for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. Towards this goal we extend a common image-oriented computational model of saliency-based visual attention to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step of a video sequence in order to obtain a compact representation of its content in the form of salient events/objects. The model has been implemented and qualitative as well as quantitative examples illustrating its performance are shown.