Stereo-vision-based moving object tracking via robust linear filtering

Pathirana, Pubudu, Bishop, Adrian and Savkin, Andrey 2007, Stereo-vision-based moving object tracking via robust linear filtering, in Proceedings of the 2007 Intelligent Sensors, Sensor Networks & Information Processing Conference, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, N.J., pp. 221-226.

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Title Stereo-vision-based moving object tracking via robust linear filtering
Author(s) Pathirana, Pubudu
Bishop, Adrian
Savkin, Andrey
Conference name International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2007 : Melbourne, Vic.)
Conference location Melbourne, Australia
Conference dates 3-6 December 2007
Title of proceedings Proceedings of the 2007 Intelligent Sensors, Sensor Networks & Information Processing Conference
Editor(s) [Unknown]
Publication date 2007
Conference series International Conference on Intelligent Sensors, Sensor Networks and Information
Start page 221
End page 226
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Summary Vision-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.
ISBN 1424415020
9781424415021
Language eng
Field of Research 010203 Calculus of Variations, Systems Theory and Control Theory
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008035

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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