Proceedings Article | 15 September 2004
KEYWORDS: Video surveillance, Video, Cameras, Visualization, Computing systems, RGB color model, Machine vision, Computer vision technology, Motion analysis, Imaging systems
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.
