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Stereo-vision-based moving object tracking via robust linear filtering
conference contribution
posted on 2007-01-01, 00:00 authored by Pubudu PathiranaPubudu Pathirana, Adrian Bishop, A SavkinVision-based tracking sensors typically provide nonlinear measurements
of the targets Cartesian position and velocity state components. In this paper we derive linear measurements using an analytical measurement conversion technique which can be used with two (or more) vision sensors. We derive
linear measurements in the target’s Cartesian position and velocity components and we derive a robust version of a linear Kalman filter. We show that our linear robust filter significantly outperforms the extended Kalman Filter. Moreover, we prove that the state estimation error is bounded.
of the targets Cartesian position and velocity state components. In this paper we derive linear measurements using an analytical measurement conversion technique which can be used with two (or more) vision sensors. We derive
linear measurements in the target’s Cartesian position and velocity components and we derive a robust version of a linear Kalman filter. We show that our linear robust filter significantly outperforms the extended Kalman Filter. Moreover, we prove that the state estimation error is bounded.
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Event
International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2007 : Melbourne, Vic.)Pagination
221 - 226Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Melbourne, AustraliaPlace of publication
Piscataway, N.J.Start date
2007-12-03End date
2007-12-06ISBN-13
9781424415021ISBN-10
1424415020Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2007, IEEETitle of proceedings
Proceedings of the 2007 Intelligent Sensors, Sensor Networks & Information Processing ConferenceUsage metrics
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