I have some questions about background subtraction with Gaussian Mixture Model (GMM)

1. What is the main application of Gaussian mixture model GMM )?
2. What is background subtraction algorithms?
3. How can I improve my GMM accuracy?
4. Which approach is used by Gaussian mixture models?

What is the main application of Gaussian mixture model GMM )?

Gaussian mixture models are extensively utilized in mining data, recognition of patterns, machine learning, and statistical analysis. In several applications, their parameters are detected using maximal likelihood and EM algorithm and are modeled as latent variables.

What is background subtraction algorithms?

The background subtraction method (BSM) is one of the most popular approaches to detecting objects. This algorithm works by comparing moving parts of a video to a background image and foreground image.

How can I improve my GMM accuracy?

You can try increasing the number of mixture components. For a 3-class problem, conventional GMM is expected to give close to 100%. If you try to identify emotion from speech signal, try increasing the duration of the speech signal during training and testing. Nagarajan, I use biosignal from the Mahnob-HCI database.

Which approach is used by Gaussian mixture models?

The GaussianMixture object implements the expectation-maximization (EM) algorithm for fitting mixture-of-Gaussian models.