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Proceedings Paper

Application of spatial likelihood functions to multicamera object localization
Author(s): Parham Aarabi
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Paper Abstract

The benefits and problems of a multi-camera object localization system utilizing Spatial Likelihood Functions (SLF) are explored. This method utilizes the angular extent of objects perceived by different cameras in order to find the region in which they intersect. This region will ideally correspond to the original location of the objects. It is shown that as long as the number of cameras is greater than the number of objects, an efficient camera fusion algorithm utilizing SLFs can be successfully employed to localize the objects. In certain situations, especially with a greater number of objects than cameras, false objects will appear among the correctly localized objects. Several different techniques to identify and remove the false objects are proposed, including a heuristic-based ray tracing approach and other multi-modal techniques. The effectiveness of the camera fusion and false object removal approaches are illustrated in the context of several examples.

Paper Details

Date Published: 22 March 2001
PDF: 9 pages
Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421104
Show Author Affiliations
Parham Aarabi, Stanford Univ. (Canada)

Published in SPIE Proceedings Vol. 4385:
Sensor Fusion: Architectures, Algorithms, and Applications V
Belur V. Dasarathy, Editor(s)

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