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

Date

2014-12-01

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Journal Title

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Volume Title

Publisher

Cambridge University Press (CUP)

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Article

ISSN

0263-5747

Format

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

Software Description

Software Language

Github

Keywords

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

DOI

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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