B. Antoni, M. Vijay and V. Nuno, “Generalized StaufferGrimson Background Subtraction for Dynamic Scenes,” Journal of Machine Vision and Applications, Vol. 22, No. 5, 2011, pp. 751-766.

has been cited by the following article:

• TITLE: Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method

  AUTHORS: Emadeldeen Noureldaim, Mohamed Jedra, Nouredine Zahid

  KEYWORDS: Component; Pixels; Gaussian Mixture Model; Principle Component Analysis; Background Model; Noise Process; Segmentation; Tracking; Kalman Filtering

  JOURNAL NAME: Intelligent Information Management, Vol.5 No.2, March 29, 2013

  ABSTRACT: In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects; the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust combination