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Gesendet: Dienstag, 12. September 2000 13:51
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Betreff: ScienceDirect - Robotics and Autonomous Systems : Expectation-based selective attention for visual monitoring and control of a robot vehicle
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Robotics and Autonomous Systems
Volume 22, Issues 3-4 Abstract
December 1997 Journal Format-PDF (1123 K)
Pages 329-344


PII: S0921-8890(97)00046-8

Expectation-based selective attention for visual monitoring and control of a robot vehicle

Shumeet Balujaa, * and Dean A. Pomerleaua

a School of Computer Science and The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue Pittsburgh, PA 15213 USA

Available online 18 June 1998.

Abstract

Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN (Pomerleau, 1993), has yielded good results in uncluttered conditions. This paper presents an artificial neural network based learning approach for handling difficult scenes which will confuse the ALVINN system. This work presents a mechanism for achieving task-specific focus of attention by exploiting temporal coherence. A saliency map, which is based upon a computed expectation of the contents of the inputs in the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important for the task, and de-emphasize those which are not.

Author Keywords: Expectation-based selective attention; Autonomous navigation; Temporal coherence; Saliency map; Artificial neural networks

Index Terms: Mobile robots; Motion control; Computer vision; Monitoring; Navigation; Neural networks; Robot learning; Expectation based selective attention; Temporal coherence; Saliency map

*Corresponding author.


Robotics and Autonomous Systems Abstract
Journal Format-PDF (1123 K)
Volume 22, Issues 3-4
December 1997
Pages 329-344


10 of 17 Article ListPreviousNext
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