Processes rely on queues to hold and prioritize cases and objects being passed between workers and teams. Consequently, analyzing queue behavior provides insights into process performance. As queue behavior is not explicitly recorded in event logs, missing information has to be restored. Existing queue mining techniques leverage queue models to restore abstractions of a queue. In this paper, we show that by treating queues and workers as objects, we can use an object-centric approach to infer missing queue information and to analyze queue behavior and performance. Evaluation on an industrial incident management process demonstrates the feasibility of the approach.