Tracking of subviral particles with automated methods enables the analysis of intracellular processes exhibited by viruses. A linear assignment problem solver and a Kalman-filter have been added to an existing particle tracking algorithm. First results produced with simulated image sequences showed that the improved algorithm is able to improve tracking results by closing gaps in the particle’s trajectories. Here we report on the evaluation of the LAP-Kalman algorithm using real fluorescence-microscopic images. The results from the original and improved algorithm have been compared to the results of manual tracking. Evaluation results indicate that the improved algorithm is capable to reconstruct missing parts of particle tracks in difficult conditions. However, the evaluation of the algorithms and the manual tracking is a complex task because of the low image contrast and high object density with intersecting tracks in the live-cell images.