Vehicle detection through machine vision is one of the most promising advanced technologies available today for dealing with the problem of urban traffic congestion. In this project an existing Wide Area Detection System (WADS) was improved for performing detection under all weather, traffic, and artifact conditions (e.g. shadows, reflections, lightning, etc. As a result of this and other related research efforts by the same team, a real-time (instead of the initially envisioned off-line) multispot breadboard WADS system was developed, installed, tested, and demonstrated in several real-life situations. The system can simultaneously detect traffic at multiple points within the field of the camera's view and emulates loop detectors. The test results to this point suggest high accuracy levels, comparable to loop detectors, while speed measurement appears to be more accurate than loops. Live demonstrations and off-line presentations generated the enthusiasm and support of practicing engineers and public officials. They also suggest that the WADS system developed in this project is the most advanced one available today. Despite this, further work remains to be done prior to production. This includes extensive field testing and validation as well as implementation of applications possibly through demonstration projects. This report describes the WADS algorithm development and testing and makes recommendations for field implementation of the technology.
Office of Safety and Traffic Operations R&D, Federal Highway Administration
Michalopoulos, Panos; Johnston, S. E.; Fundakowski, R. A.; Fitch, R. C..
Wide area detection system (WADS) : image recognition algorithms.
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