Fusion of multiple sensor types in computer vision systems
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Authors
Mayo, Donald R.
Subjects
Advisors
Kolsch, Mathias
Squire, Kevin
Date of Issue
2007-09
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
This research provides analysis of several approaches to the fusion of multiple dissimilar sensors to supplement simple color vision detection and recognition. Non-visible sensor systems can enhance computer vision systems. Our research investigates using thermal infrared (IR) sensors in combination with color data for object detection and recognition. We analyze several types of high-level and low-level sensor fusion to compare error rates with raw color and raw IR error rates in detection and recognition of vehicles in a scene. Principal components analysis is used to reduce the dimensionality of sensor input data in order to discard non-essential data, while preserving data important to classification. One recognition method showing promise is to exploit the strength of non-visible information (low light, shadows, etc.) to reduce the search space for color data by replacing the V channel in the HSV color sensor data with IR. For detection, one method showing promise is replacement or averaging of the dominant color channel with IR.
Type
Thesis
Description
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xvi, 89 p. : ill. (chiefly col.) ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.