The capability of determining the quality of target detections is important for applications using smart cameras, such as autonomous robotics and surveillance. We propose to estimate the quality of target detections by integrating the target location uncertainty over polygonal domains, which represent the fields of view of the cameras. We define a framework based on numerical integration that easily accommodates multiple models for uncertainty and fields of view. We perform quadrature-based integration combined with importance sampling to provide accurate quality estimations while reducing the computational cost. The proposed method outperforms alternative approaches in terms of estimation accuracy and execution time. We validate the proposed approach with a recent distributed multi-camera multi-target tracker and improved it by considering realistic fields of view. Results demonstrate the effectiveness of the proposed method in decreasing the state estimation error.