Visual surveillance for traffic systems requires short processing time, low processing cost and high reliability. Under
those requirements, image processing technologies offer a variety of systems and methods for Intelligence
Transportation Systems (ITS) as a platform for traffic Automatic Incident Detection (AID). There exist two classes of
AID methods mainly studied: one is based on inductive loops, radars, infrared sonar and microwave detectors and the
other is based on video images. The first class of methods suffers from drawbacks in that they are expensive to install
and maintain and they are unable to detect slow or stationary vehicles. Video sensors, on the other hand, offer a
relatively low installation cost with little traffic disruption during maintenance. Furthermore, they provide wide area
monitoring allowing analysis of traffic flows and turning movements, speed measurement, multiple-point vehicle counts,
vehicle classification and highway state assessment, based on precise scene motion analysis.
This paper suggests the utilization of traffic models for real-time vision-based traffic analysis and automatic incident
detection. First, the traffic flow variables, are introduced. Then, it is described how those variables can be measured from
traffic video streams in real-time. Having the traffic variables measured, a robust automatic incident detection scheme is
suggested. The results presented here, show a great potential for integration of traffic flow models into video based
intelligent transportation systems. The system real time performance is achieved by utilizing multi-core technology using
standard parallelization algorithms and libraries (OpenMP, IPP).