Visual Monitoring of Railroad Grade Crossing

There are approximately 261,000 rail crossings in the United States according to the studies by the National Highway Traffic Safety Administration (NHTSA) and Federal Railroad Administration (FRA). From 1993 to 1998, there were over 25,000 highway-rail crossing incidents involving motor vehicles averaging 4,167 incidents a year. In this paper, we present a real-time computer vision system for the monitoring of the movement of pedestrians, bikers, animals and vehicles at railroad intersections. The video is processed for the detection of uncharacteristic events, triggering an immediate warning system. In order to recognize the events, the system first performs robust object detection and tracking. Next, a classification algorithm is used to determine whether the detected object is a pedestrian, biker, group or a vehicle, allowing inferences on whether the behavior of the object is characteristic or not. Due to the ubiquity of low cost, low power, and high quality video cameras, increased computing power and memory capacity, the proposed approach provides a cost effective and scalable solution to this important problem. Furthermore, the system has the potential to significantly decrease the number of accidents and therefore the resulting deaths and injuries that occur at railroad crossings. We have field tested our system at two sites, a rail-highway grade crossing, and a trestle located in Central Florida, and we present results on six hours of collected data.