izvorni znanstveni rad
Development of computing power and cheap video cameras enabled today’s traffic management systems to include more cameras and computer vision based applications for monitoring and control of road transportation systems. 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 macroscopic traffic parameters). The system is based on Local Binary Pattern image features classification with a cascade of Gentle Adaboost 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 and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by means of evaluating execution time and accuracy.
Computer vision; local binary patters; extended Kalman Filter; road vehicles; traffic parameters
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