Real-time Occupancy Correction Method for 3D Stereovision Counting Cameras

In this poster, we present an occupancy count correction method - PreCount that corrects the count errors of camera sensing technologies in real-time. PreCount utilizes supervised machine learning approach to learn error patterns from previous corrections alongside some contextual factors that are responsible for the propagation of these errors. In our evaluation, we compare PreCount with state-of-art methods using the normalized root mean squared error metric (NRMSE) with datasets from four building cases. The obtained evaluation results using ground truth data indicates that PreCount can achieve an error reduction of 68% when compared to raw counts and state-of-art methods.