We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An automated tracking algorithm promises to provide a complete analysis of noisy microscopy data. In this talk, we will get exposed to two such algorithms. The first algorithmic implementation adopts statistical techniques from a Bayesian random set point of view. Instead of considering each individual organelle, we examine a random set whose members are the organelle states and we establish a Bayesian filtering algorithm involving such set-states. The second method combines stochastic filtering estimates of the displacement field with the topological properties on the space of trajectories. Both methods were used to analyze real data.