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An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel


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1 Rabat GSCM-LRIT Laboratory Associate Unit to CNRST (URAC 29), Mohammed V University, BP 1014, Rabat, Morocco
 

We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image regionwith a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.
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  • An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel

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Authors

Hamd Ait Abdelali
Rabat GSCM-LRIT Laboratory Associate Unit to CNRST (URAC 29), Mohammed V University, BP 1014, Rabat, Morocco
Fedwa Essannouni
Rabat GSCM-LRIT Laboratory Associate Unit to CNRST (URAC 29), Mohammed V University, BP 1014, Rabat, Morocco
Leila Essannouni
Rabat GSCM-LRIT Laboratory Associate Unit to CNRST (URAC 29), Mohammed V University, BP 1014, Rabat, Morocco
Driss Aboutajdine
Rabat GSCM-LRIT Laboratory Associate Unit to CNRST (URAC 29), Mohammed V University, BP 1014, Rabat, Morocco

Abstract


We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image regionwith a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.