Motion Saliency Maps from Spatiotemporal Filtering

For artificial systems acting and perceiving in a dynamic world a core ability is to focus on aspects of the environment that can be crucial for the task at hand. Perception in autonomous systems needs to be filtered by a biologically inspired selective ability, therefore attention in dynamic settings is becoming a key research issue. In this paper we present a model for motion salience map computation based on spatiotemporal filtering. We extract a measure of coherent motion energy and select by the center-surround mechanism relevant zones that accumulate most energy and therefore contrast with surroundings in a given time slot. The method was tested on synthetic and real video sequences, supporting biological plausibility.