LAUSR

Multiple-particle tracking-by-detection is a widely investigated issue in image processing. This article presents approaches to detecting and tracking various refuse-derived fuel particles in an industrial environment using a plenoptic camera system, which is able to yield 2-D gray value information and 3-D point clouds with noticeable fluctuations. The presented approaches, including an innovative combined detection method and a postprocessing framework for multiple-particle tracking, aim at making the most of the acquired 2-D and 3-D information to deal with the fluctuations of the measuring system. The proposed novel detection method fuses the captured 2-D gray value information and 3-D point clouds, which is superior to applying single information. Subsequently, the particles are tracked by the linear Kalman filter, the 2.5-D global nearest neighbor (GNN), and the 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 postprocessing process. The developed postprocessing 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 manually labeled ground-truth datasets to prove their availability and adequacy as well. The presented combined detection method provides the highest F1-score, and the proposed postprocessing framework enhances the tracking performance significantly with regard to several recommended evaluation indices.