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Selective Visual Attention for Object Detection on a Legged Robot

Daniel Stronger and Peter Stone. Selective Visual Attention for Object Detection on a Legged Robot. In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp. 158–170, Springer Verlag, Berlin, 2007.
Some videos referenced in the paper.

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Abstract

Autonomous robots can use a variety of sensors, such as sonar, laser range finders, and bump sensors, to sense their environments. Visual information from an onboard camera can provide particularly rich sensor data. However, processing all the pixels in every image, even with simple operations, can be computationally taxing for robots equipped with cameras of reasonable resolution and frame rate. This paper presents a novel method for a legged robot equipped with a camera to use selective visual attention to efficiently recognize objects in its environment. The resulting attention-based approach is fully implemented and validated on an Aibo ERS-7. It effectively processes incoming images 50 times faster than a baseline approach, with no significant difference in the efficacy of its object detection.

BibTeX

@incollection(LNAI2006-dan,
        author="Daniel Stronger and Peter Stone",
        title="Selective Visual Attention for Object Detection on a Legged Robot",
        booktitle= "{R}obo{C}up-2006: Robot Soccer World Cup {X}",
        Editor="Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi",
        Publisher="Springer Verlag",address="Berlin",year="2007",
        issn="0302-9743",
        isbn="978-3-540-74023-0",
        series="Lecture Notes in Artificial Intelligence",      
	volume="4434",
        pages="158--170",
        abstract={
                  Autonomous robots can use a variety of sensors, such
                  as sonar, laser range finders, and bump sensors, to
                  sense their environments.  Visual information from
                  an onboard camera can provide particularly rich
                  sensor data.  However, processing all the pixels in
                  every image, even with simple operations, can be
                  computationally taxing for robots equipped with
                  cameras of reasonable resolution and frame rate.
                  This paper presents a novel method for a legged
                  robot equipped with a camera to use selective visual
                  attention to efficiently recognize objects in its
                  environment.  The resulting attention-based approach
                  is fully implemented and validated on an Aibo ERS-7.
                  It effectively processes incoming images 50 times
                  faster than a baseline approach, with no significant
                  difference in the efficacy of its object detection.
        },
        wwwnote={Some <a href="http://www.cs.utexas.edu/~AustinVilla/?p=research/selective-vision">videos</a> referenced in the paper.},
)

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