We present a recursion of the probability of target visibility and its applications to analysis of track life and termination in the context of Global Nearest Neighbour (GNN) approach and Probability Hypothesis Density (PHD) filter. In the presence of uncertainties brought about by clutter; decisions to retain a track, terminate it or initialise a new track are based on probability, rather than on distance criterion or estimation error. The visibility concept is introduced into a conventional data-association-oriented multitarget tracker, the GNN; and a random finite set based-tracker, the PHD filter, to take into account instances when targets become invisible or occluded by obstacles. We employ the natural logarithmof the Dynamic Error Spectrum to assess the performance of the trackers with and without probability of visibility incorporated. Simulation results show that the performance of the GNN tracker with visibility concept incorporated is significantly enhanced.