Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)
Rika RosnellyLecturer, Faculty of Engineering and Computer Science, University of Potensi Utama, Medan, Indonesia. rikarosnelly@gmail.com0000-0002-0407-5160
Bob Subhan RizaLecturer, Faculty of Engineering and Computer Science, University of Potensi Utama, Medan, Indonesia. bob.potensi@gmail.com0000-0001-6358-9412
Linda WahyuniLecturer, Faculty of Engineering and Computer Science, University of Potensi Utama, Medan, Indonesia. lindawahyuni391@gmail.com0000-0003-2027-7616
S. Edy Victor HaryantoLecturer, Faculty of Engineering and Computer Science, University of Potensi Utama, Medan, Indonesia. edyvictor@gmail.com0000-0001-8317-3545
Annas PrasetioInstructor, Faculty of Engineering and Computer Science, University of Potensi Utama, Medan, Indonesia. annasprasetio45@gmail.com0000-0002-1734-928X
Motion tracking apps are used for a lot of different things, like finding traffic jams and counting the number of cars going through a traffic light. The datasets come from many places on the internet, like YouTube and public dataset archives. There are about 20 videos that are tagged with the words "traffic" and "traffic camera video" and run for 10 to 30 seconds. The Gaussian Mixture Models (GMM) method is the proposed model. It separates the background from the tracked object, which is needed to do motion tracking. Then, the GMM method groups pixel data based on the background color of each pixel. After the cluster is made, the input is matched as a distribution, with the most common distribution used as the background. The analysis was done using MATLAB2019B. The results of this study show that the GMM method can adapt to the background. This is shown by the fact that testing of some of the given conditions went well.