Evaluation of the Accuracy of a Computer-Vision Based Crowd Monitoring System

Computer vision systems can be used to measure pedestrian flow rates, occupancy levels and queue times. It is difficult to assess the accuracy of such methods because the ground truth can be difficult to establish. Human counting is equally prone to error, even when using video recordings with no time constraints and the support of sophisticated software. In this report, we consider how errors may arise directly from the images recorded by the cameras, due to both occlusion of people and image distortion due to a fisheye lens. We also develop a statistical model of human counting errors and attempt to estimate human accuracy from data. Finally, we attempt to relate human and computer accuracy on the basis of simplifying statistical approximations