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

Date

2014-12-01

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Publisher

Cambridge University Press (CUP)

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Type

Article

ISSN

0263-5747

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Citation

Amamra A, Aouf N. (2016) Real-time multiview data fusion for object tracking with RGBD sensors, Robotica, Volume 34, Issue 8, August 2016, pp. 1855-1879

Abstract

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.

Description

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Keywords

RGBD, Real-time, Tracking, Multiview, Kalman filter, Robust H∞, Covariance intersection, GPU

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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