This paper tackles the problem of coordinated vision-based tracking of a ground target by a fleet of multiple UAVs that exchange information over a supporting time-varying network. The objective of this work is to formulate decentralized control algorithms that enable multiple vehicles to follow a target while coordinating their phase separation. A typical scenario involves multiple aerial surveillance UAVs which are required to monitor a moving ground object (target tracking), while maintaining a desired inter-vehicle separation (coordination). To solve the tracking problem, the yaw rate of each vehicle is used as control input, while the ground speeds are adjusted to achieve coordination. It is assumed that the UAVs are equipped with an internal autopilot, which is able to track yaw rate and ground speed commands. The performance of the coordinated vision-based tracking algorithm is evaluated as a function of target's velocity, tracking performance of the onboard autopilot, and the quality of service of the communication network.