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Low-cost, vision-based techniques for the quantitative analysis of road traffic data in real-time

Sgouros N., Kabolis P.

Image Processing & Communications

2000

Vol. 6, no 1-2

13-22

Quantitative analysis of traffic data is concerned with the extraction of traffic parameters such as vehicle counts, lane occupancy, speed measurement etc. This article describes a set of low-cost, vision-based techniques for the quantitative analysis of road traffic data in real-time. Analysis consists of two phases; vehicle detection and extraction of traffic parameters. Vehicle detection applies a number of image processing techniques such as background subtraction, binary thresh-olding, morphological processing and blob coloring for the detection of the vehicles passing through a user-specified region in a road. The vehicle detection phase ends with the estimation of a set of characteristic features for each vehicle such as its area, its center of mass and its perimeter. During the next phase these features are fed to a novel tracking algorithm that uniquely identifies each vehicle in time, estimates its speed and determines the traffic flow in the region of interest. All these techniques have been implemented in a traffic monitoring system. The system runs on top of a PC with an AMD 450 K6-2 processor. It has been tested in real traffic conditions and it has achieved a 93rate in real-time speed detection.