Amamra, AbdenourAouf, Nabil2023-09-202023-09-202014-12-01Amamra A, Aouf N. (2016) Real-time multiview data fusion for object tracking with RGBD sensors, Robotica, Volume 34, Issue 8, August 2016, pp. 1855-18790263-5747https://doi.org/10.1017/S026357471400263Xhttps://dspace.lib.cranfield.ac.uk/handle/1826/20259This 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.enAttribution-NonCommercial-NoDerivatives 4.0 InternationalRGBDReal-timeTrackingMultiviewKalman filterRobust H∞Covariance intersectionGPUArticle1469-8668