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Multimodal saliency-based attention for object-based scene analysis

: Schauerte, B.; Kühn, Benjamin; Kroschel, Kristian; Stiefelhagen, Rainer

Postprint urn:nbn:de:0011-n-1987664 (1.2 MByte PDF)
MD5 Fingerprint: e49db3e7861791eaa6731eb6004db37c
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Erstellt am: 19.6.2012

Amato, N.M. ; Institute of Electrical and Electronics Engineers -IEEE-; Robotics Society of Japan -RSJ-:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 : San Francisco, California, USA, 25 - 30 September 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-61284-455-8
ISBN: 978-1-61284-454-1
S.1173-1179 (Vol.2)
International Conference on Intelligent Robots and Systems (IROS) <2011, San Francisco/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Multimodal attention is a key requirement for humanoid robots in order to navigate in complex environments and act as social, cognitive human partners. To this end, robots have to incorporate attention mechanisms that focus the processing on the potentially most relevant stimuli while controlling the sensor orientation to improve the perception of these stimuli. In this paper, we present our implementation of audio-visual saliency-based attention that we integrated in a system for knowledge-driven audio-visual scene analysis and object-based world modeling. For this purpose, we introduce a novel isophote-based method for proto-object segmentation of saliency maps, a surprise-based auditory saliency definition, and a parametric 3-D model for multimodal saliency fusion. The applicability of the proposed system is demonstrated in a series of experiments.