  Development of computing power and cheap video cameras enabled today's
traffic management systems to include more cameras and computer vision
applications for transportation system monitoring and control. Combined with
image processing algorithms cameras are used as sensors to measure road traffic
parameters like flow volume, origin-destination matrices, classify vehicles,
etc. In this paper we propose a system for measurement of road traffic
parameters (basic motion model parameters and macro-scopic traffic parameters).
The system is based on Local Binary Pattern (LBP) image features classification
with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle
existence and its location in an image. Additionally, vehicle tracking and
counting in a road traffic video is performed by using Extended Kalman Filter
(EKF) and virtual markers. The newly proposed system is compared with a system
based on background subtraction. Comparison is performed by the means of
execution time and accuracy.
