Real-time multiview data fusion for object tracking with RGBD sensors
This work addresses real-time object tracking for applications like surveillance or autonomous systems, but it is incremental as it builds on existing multiview and RGBD methods.
The paper tackles the problem of accurately tracking moving vehicles using a multiview RGBD camera setup, achieving real-time operation at 25 frames per second with five cameras and demonstrating robustness against measurement uncertainties.
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.