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

Emadeldeen Noureldaim, Mohamed Jedra, Nouredine Zahid

Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco.

DOI: 10.4236/iim.2013.52006                

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

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

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