Motion-based background subtraction and panoramic mosaicing for freight train analysis

We propose a new motion-based background removal technique which along with panoramic mosaicing forms the core of a vision system we have developed for analyzing the loading efficiency of intermodal freight trains. This analysis is critical for estimating the aerodynamic drag caused by air gaps present between loads in freight trains. The novelty of our background removal technique lies in using conventional motion estimates to design a cost function which can handle challenging textureless background regions, e.g. clear blue sky. Supplemented with domain knowledge, we have built a system which has outperformed some recent background removal methods applied to our problem. We also build an orthographic mosaic of the freight train allowing identification of load types and gap lengths between them. The complete system has been installed near Sibley, Missouri, US and processes about 20-30 (5-10 GB/train video data depending on train length) trains per day with high accuracy.