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

• Main Authors: Mohd Aris, Nor Afiqah, Jamaian, Siti Suhana
• Format: Article
• Language: English
• Published: 2023
• Subjects: TJ170-179 Mechanics applied to machinery. Dynamics
• Online Access: http://eprints.uthm.edu.my/10246/1/J15807_0082aff3a6d68eae3f24b79bc11d56f6.pdf; http://eprints.uthm.edu.my/10246/; https://doi.org/10.11591/ijai.v12.i3.pp1007-1018

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.