We propose a method for calibrating the topology of distributed pan-tilt cameras (i.e. the structure of routes among and within FOVs) and its probabilistic model. To observe as many objects as possible for as long as possible, pan-tilt control is an important issue in automatic calibration as well as in tracking. In a calibration period, each camera should be controlled towards an object that goes through an unreliable route whose topology is not calibrated yet. This camera control allows us to efficiently establish the topology model. After the topology model is established, the camera should be directed towards the route with the biggest possibility of object observation. We propose a camera control framework based on the mixture of the reliability of the estimated routes and the probability of object observation. This framework is applicable both to camera calibration and object tracking by adjusting weight variables. Experiments demonstrate the efficiency of our camera control scheme for establishing the camera topology model and tracking objects as long as possible.