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The objective of this paper is to compare the performance of threebackground-modeling algorithms in segmenting and detecting vehicles in highwaytraffic videos. All algorithms are available in OpenCV and were all coded inPython. We analyzed seven videos, totaling 2 hours of recording. To compare thealgorithms, we created 35 ground-truth images, five from each video, and weused three different metrics: accuracy rate, precision rate, and processingtime. By using accuracy and precision, we aim to identify how well thealgorithms perform in detection and segmentation, while using the processingtime to evaluate the impact on the computational system. Results indicate thatall three algorithms had more than 90% of precision rate, while obtaining anaverage of 80% on accuracy. The algorithm with the lowest impact on processingtime allowed the computation of 60 frames per second.

A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos