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Proceedings Paper

Task-selection and task-merging for directional and vision-based sensors
Author(s): Jacob Everist; T. Nathan Mundhenk; Chris Landauer; Kirstie Bellman
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Paper Abstract

Many vision research projects involve a sensor or camera doing one thing and doing it well. Fewer research projects have been done involving a sensor trying to satisfy simultaneous and conflicting tasks. Satisfying a task involves pointing the sensor in the direction demanded by the task. We seek ways to mitigate and select between competing tasks and also, if possible, merge the tasks together to be simultaneously achieved by the sensor. This would make a simple pan-tilt camera a very powerful instrument. These two approaches are task-selection and task-merging respectively. We built a simple testbed to implement our task-selection and task-merging schemes. We use a digital camera as our sensor attached to pan and tilt servos capable of pointing the sensor in different directions. We use three different types of tasks for our research: target tracking, surveillance coverage, and initiative. Target tracking is the task of following a target with a known set of features. Surveillance coverage is the task of ensuring that all areas of the space are routinely scanned by the sensor. Initiative is the task of focusing on new things of potential interest should they appear in the course of other activities. Given these heterogeneous task descriptions, we achieve task-selection by assigning priority functions to each task and letting the camera select among the tasks to service. To achieve task-merging, we introduce a concept called "task maps" that represent the regions of space the tasks wish to attend with the sensor. We then merge the task maps and select a region to attend that will satisfy multiple tasks at the same time if possible.

Paper Details

Date Published: 24 October 2005
PDF: 9 pages
Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 600609 (24 October 2005); doi: 10.1117/12.630957
Show Author Affiliations
Jacob Everist, Univ. of Southern California (United States)
T. Nathan Mundhenk, Univ. of Southern California (United States)
Chris Landauer, The Aerospace Corp. (United States)
Kirstie Bellman, The Aerospace Corp. (United States)


Published in SPIE Proceedings Vol. 6006:
Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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