Pseudo-realtime activity detection for railroad grade crossing safety

Abstract

It is important to understand the factors underlying grade crossing crashes, and to examine potential solutions. We have installed a camera in front of a locomotive to examine grade crossing accidents (or near accidents). We present a computer vision system that automatically extracts possible near accidents scenes by detecting the activity of vehicles crossing in front of the train after the signals are ignited. We presented a fast algorithm to detect moving objects that is recorded by a moving camera with minimal computation. The moving object is detected by 1) estimating ego-motion of the camera and 2) detecting and tracking feature points whose motion is inconsistent with the camera motion. We introduce a pseudo-realtime ego-motion (camera motion) estimation method with a robust optimization algorithm. We present experiments on ego-motion estimation and moving object detection. Our algorithm works in pseudo-realtime and we expect that our algorithm can be applied to realtime applications, such as collision warning, in the near future with the development of hardware technology.