We design a path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple ground targets based on the theory of partially observable Markov decision processes (POMDPs). We demonstrate the power and flexibility of the POMDP framework by showing that a variety of features of interest are easy to incorporate into the framework by plugging in the appropriate models. Specifically, in this paper we show how to incorporate the following features by appropriately formulating the POMDP action space, transition law, and objective function: 1) control UAVs with both forward acceleration and bank angle subject to constraints; 2) account for the effect of wind disturbance on UAVs; and 3) mitigate track swaps.