hodgson-robustvideoultrasonicfusion-2007.pdf (1.25 MB)
Robust video/ultrasonic fusion-based estimation for automotive applications
journal contribution
posted on 2007-07-01, 00:00 authored by Pubudu PathiranaPubudu Pathirana, Allan Lim, A Savkin, Peter HodgsonPeter HodgsonIn this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.
History
Journal
IEEE transactions on vehicular technologyVolume
56Issue
4Pagination
1631 - 1639Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Piscataway, N.J.Publisher DOI
ISSN
0018-9545eISSN
1939-9359Language
engNotes
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Publication classification
C1 Refereed article in a scholarly journalCopyright notice
2007, IEEEUsage metrics
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