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Real-time multiview data fusion for object tracking with RGBD sensors

Published online by Cambridge University Press:  01 December 2014

Abdenour Amamra  and
Nabil Aouf
Abdenour Amamra*
Affiliation:
Centre For Electronic Warfare, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, SN6 8LA.
Nabil Aouf
Affiliation:
Centre For Electronic Warfare, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, SN6 8LA.
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a new approach to accurately track a moving vehicle with a multiview setup of red–green–blue depth (RGBD) cameras. We first propose a correction method to eliminate a shift, which occurs in depth sensors when they become worn. This issue could not be otherwise corrected with the ordinary calibration procedure. Next, we present a sensor-wise filtering system to correct for an unknown vehicle motion. A data fusion algorithm is then used to optimally merge the sensor-wise estimated trajectories. We implement most parts of our solution in the graphic processor. Hence, the whole system is able to operate at up to 25 frames per second with a configuration of five cameras. Test results show the accuracy we achieved and the robustness of our solution to overcome uncertainties in the measurements and the modelling.

Keywords

RGBDReal-timeTrackingMultiviewKalman filterRobust H∞Covariance intersectionGPU
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1855 - 1879
Copyright
Copyright © Cambridge University Press 2014 

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