We introduce the Scalable Clustered Camera System, a peer-to-peer multi-camera system for multi-object tracking, where different CPUs are used to process inputs from distinct cameras. Instead of transferring control of tracking jobs from one camera to another, each camera in our system performs its own tracking and keeps its own tracks for each target object, thus providing fault tolerance. A fast and robust tracking method is proposed to perform tracking on each camera view, while maintaining consistent labeling. In addition, we introduce a new communication protocol, where the decisions about when and with whom to communicate are made such that frequency and size of transmitted messages are minimized. This protocol incorporates variable synchronization capabilities, so as to allow flexibility with accuracy tradeoffs. We discuss our implementation, consisting of a parallel computing cluster, with communication between the cameras performed by MPI. We present experimental results which demonstrate the success of the proposed peer-to-peer multi-camera tracking system, with accuracy of 95% for a high frequency of synchronization, as well as a worst-case of 15 frames of latency in recovering correct labels at low synchronization frequencies.