Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Mohd Aris, Nor Afiqah and Jamaian, Siti Suhana (2023) Background subtraction challenges in motion detection using Gaussian mixture model: a survey. IAES International Journal of Artificial Intelligence, 12 (3). pp. 1007-1018. ISSN 2252-8938

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Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.

Item Type: Article
Uncontrolled Keywords: Background-foreground detection Background subtraction Computer vision Gaussian mixture model Motion detection
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ170-179 Mechanics applied to machinery. Dynamics
Divisions: Faculty of Applied Science and Technology > Department of Postgraduate
Depositing User: Mr. Mohamad Zulkhibri Rahmad
Date Deposited: 25 Oct 2023 07:22
Last Modified: 25 Oct 2023 07:22

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