Random Finite Sets (RFS) offer a diligent formalism for tracking an unknown number of targets with multiple sensors. The Probability Hypothesis Density (PHD) filter, and its Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) implementations, provide tractable Bayesian Filtering methods that propagate the first order moment of the RFS probability density. A feature of the PHD filters is that they do not require association to complete their correction step. This, we believe, should constitute a significant advantage, especially in scenarios of high false alarm rates and track intersections, which can easily compromise most observerpredictor methods that must perform association to carry out their correction step. To test this hypothesis, we compare the performance of the GM-PHD to the traditional Kalman (KF) and SMC filters for visual tracking of multiple targets in moderate to heavy false alarm rate scenarios. Our tracking and association performance results seem to support this hypothesis.