3-D Refuse-Derived Fuel Particle Tracking-by-Detection Using a Plenoptic Camera System

Abstract

Multiple particle tracking-by-detection is a widely investigated issue in image processing. The paper presents approaches to detecting and tracking various refuse-derived fuel particles in a industrial environment using a plenoptic camera system, which is able to yield 2D gray value information and 3D point clouds with noticeable fluctuations. The presented approaches, including an innovative combined detection method and a post-processing framework for multiple particle tracking, aim at making the most of the acquired 2D and 3D information to deal with the fluctuations of the measuring system. The proposed novel detection method fuses the captured 2D gray value information and 3D point clouds, which is superior to applying single information. Subsequently, the particles are tracked by the linear Kalman filter and 2.5D global nearest neighbor (GNN) and joint probabilistic data association (JPDA) approach, respectively. As a result of several inaccurate detection results caused by the measuring system, the initial tracking results contain faulty and incomplete tracklets that entail a post-processing process. The developed post-processing approach based merely on particle motion similarity benefits a precise tracking performance by eliminating faulty tracklets, deleting outliers, connecting tracklets, and fusing trajectories. The proposed approaches are quantitatively assessed with manuelly labeled ground truth datasets to prove their availability and adequacy as well. The presented combined detection method provides the highest F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score, and the proposed post-processing framework enhances the tracking performance significantly with regard to several recommended evaluation indices.