Pseudo-realtime activity detection for railroad grade crossing safety

ZuWhan Kim, T.E. Cohn
Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems  
It is important to understand the factors underlying gradecrossing 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-accident scenes by detecting the activity of vehicles crossing in front of the train after signals are ignited. We present a fast algorithm to detect moving objects recorded by a moving camera with
more » ... mal computation. The moving object is detected by: 1) estimating the ego motion of the camera and 2) detecting and tracking feature points whose motion is inconsistent with the camera motion. We introduce a pseudoreal-time 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 pseudoreal-time and we expect that our algorithm can be applied to real-time applications such as collision warning in the near future, with the development of hardware technology.
doi:10.1109/itsc.2003.1252705 fatcat:ght7ef5wizgy3o2qn66ozbzaum