Abstract-Large variations in the execution times of algorithms characterize many cyber-physical systems (CPS). For example, variations arise in the case of visual object-tracking tasks, whose execution times depend on the contents of the current field of view of the camera. In this paper, we study such a scenario in a small Unmanned Aerial Vehicle (UAV) system with a camera that must detect objects in a variety of conditions ranging from the simple to the complex. Given resource, weight and size constraints, such cyber-physical systems do not have the resources to satisfy the hard-real-time requirements of safe flight along with the need to process highly variable workloads at the highest quality and resolution levels. Hence, tradeoffs have to be made in real-time across multiple levels of criticality of running tasks and their operating points. Specifically, the utility derived from tracking an increasing number of objects may saturate when the mission software can no longer perform the required processing on each individual object. In this paper, we evaluate a new approach called ZS-QRAM (ZeroSlack QoS-based Resource Allocation Model) that maximizes the UAV system utility by explicitly taking into account the diminishing returns on tracking an increasing number of objects. We perform a detailed evaluation of our approach on our UAV system to clearly demonstrate its benefits.