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Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method
Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
- 1 Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
- 2 Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
- 3 Laboratory of Conception and Systems, Faculty of Sciences, Mohamed V University, Rabat, Morocco
Intelligent Information Management·Volume 05 (2013)·Pages 42–47·Published 29 March 2013·DOI10.4236/iim.2013.52006
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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
KeywordsComponentPixelsGaussian Mixture ModelPrinciple Component AnalysisBackground ModelNoise ProcessSegmentationTrackingKalman Filtering
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