The Intelligent Vision Agent System, IVAS, is a system for automatic target detection, identification and information processing for use in human activities surveillance. This system consists of multiple sensors, and with control of their deployment and autonomous servo. Finding the optimal configuration for these sensors in order to capture the target objects and their environment to a required specification is a crucial problem. With a stereo pair of sensors, the 3D space can be discretized by an iso-disparity surface, and the depth reconstruction accuracy of the space is closely related to the iso-disparity curve positions. This paper presents a method to enable planning the position of these multiple stereo sensors in indoor environments. The proposed method is a mathematical geometry model, used to analyze the iso-disparity surface. We will show that the distribution of the iso-disparity surface and the depth reconstruction accuracy are controllable by the parameters of such model. This model can be used to dynamically adjust the positions, poses and baselines lengths of multiple stereo pairs of cameras in 3D space in order to get sufficient visibility and accuracy for surveillance tracking and 3D reconstruction. We implement the model and present uncertainty maps of depth reconstruction calculated while varying the baseline length, focal length, stereo convergence angle and sensor pixel length. The results of these experiments show how the depth reconstruction uncertainty depends on stereo pair’s baseline length, zooming and sensor physical properties.