Automated extraction of weather variables from camera imagery
August 18, 2005
Thousands of traffic and safety monitoring cameras are deployed or are being deployed all across the country and throughout the world. These cameras serve a wide range of uses from monitoring building access to adjusting timing cycles of traffic lights at clogged intersections. Currently, these images are typically viewed on a wall of monitors in a traffic operations or security center where observers manually monitor potentially hazardous or congested conditions and notify the appropriate authorities. However, the proliferation of camera imagery taxes the ability of the manual observer to track and respond to all incidents. In addition, the images contain a wealth of information, including visibility, precipitation type, road conditions, camera outages, etc., that often goes unreported because these variables are not always critical or go undetected. Camera deployments continue to expand and the corresponding rapid increases in both the volume and complexity of camera imagery demand that automated algorithms be developed to condense the discernable information into a form that can be easily used operationally by users. MIT Lincoln Laboratory (MIT/LL) under funding from the Federal Highway Administration (FHWA) is investigating new techniques to extract weather and road condition parameters from standard traffic camera imagery. To date, work has focused on developing an algorithm to measure atmospheric visibility and prove the algorithm concept. The initial algorithm examines the natural edges within the image (the horizon, tree lines, roadways, permanent buildings, etc) and performs a comparison of each image with a historical composite image. This comparison enables the system to determine the visibility in the direction of the sensor by detecting which edges are visible and which are not. A primary goal of the automated camera imagery feature extraction system is to ingest digital imagery with limited specific site information such as location, height, angle, and visual extent, thereby making the system easier for users to implement. There are, of course, many challenges in providing a reliable automated estimate of the visibility under all conditions (camera blockage/movement, dirt/raindrops on lens, etc) and the system attempts to compensate for these situations. This paper details the work-to-date on the visibility algorithm and defines a path for further development of the overall system.