Computer Vision Detection System for Network Model Validation

Abstract

A computer vision-based traffic detection system has been developed and tested that is capable of uniquely identifying vehicles in a traffic network from video images and later reidentifying them at other detection sites in the network. This capability may serve as the basis for deterministic validation of microscopic network flow models, origin-destination tables, and travel-time tables. The system is referred to as the video-based vehicle signature analysis and tracking system. With video cameras used as primary sensors, detection modules generate a numeric video signature vector (VSV) for each vehicle and transmit them to a central Internet-connected correlation computer via a public low-power wireless network. The correlation computer attempts to match vehicles, as represented by their VSVs, generated at successive detection sites to enable a sampled real-time microscopic flow representation of a freeway network. Test results indicate 93.6 percent accuracy in correctly reidentifying vehicles at successive sites. The tendency of the system to incorrectly match different vehicles at successive sites was measured at 0.0116 percent. Potential advantages are low cost in widespread deployment, simplicity and reliability of detection, minimal bandwidth and storage requirements for transmission of the VSV, and reasonable identification ability without violation of privacy rights. The primary disadvantages are the requirement to place video cameras above each traffic lane and video imaging limitations related to reliance on ambient illumination.