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