Vehicle Detection Using Machine Learning Model with the  Gaussian Mixture Model (GMM)

Rosnelly, Rika

Please use this identifier to cite or link to this item: http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/5556

Title:	Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)
Authors:	Rosnelly, Rika
Keywords:	Vehicle Detection, Gaussian Mixture Models, MATLAB2019B, Background, Motion Tracking.
Issue Date:	2023
Abstract:	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.
Description:	The rapid growth and development of digital technology and the availability of video capture-based devices such as digital cameras and mobile phones with cameras are driving the explosive rise of network storage devices[1–3]. This is consistent with the nearly daily increase in the number of cars, but it is not accompanied by major volume changes [4]. Consequently, with a substantial increase in the number of vehicles and a constant volume of roadways, there will be an accumulation of vehicles, which ultimately leads to congestion [5, 6]. Congestion is a prevalent issue in cities, which in turn leads cities to become inefficient and generates considerable economic losses due to a number of factors, including the ratio of transportation infrastructure to land area, an inadequate network, unregulated spatial planning, and vehicle expansion
URI:	http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/5556
Appears in Collections:	A Paper

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