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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Belardinelli, A., Pirri, F., & Carbone, A. (2009). Motion saliency maps from spatiotemporal filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5395 LNAI, pp. 112–123). https://doi.org/10.1007/978-3-642-00582-4_9
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