An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel | Zendy

Hamd Ait Abdelali | Zendy; Fedwa Essannouni | Zendy; Leila Essannouni | Zendy; Driss Aboutajdine | Zendy

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An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel

Author(s) -

Hamd Ait Abdelali,

Fedwa Essannouni,

Leila Essannouni,

Driss Aboutajdine

Publication year - 2016

Publication title -

modelling and simulation in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 20

eISSN - 1687-5591

pISSN - 1687-5605

DOI - 10.1155/2016/2592368

Subject(s) - kalman filter , kernel (algebra) , computer vision , histogram , artificial intelligence , video tracking , tracking (education) , computer science , object (grammar) , product (mathematics) , kernel adaptive filter , tracking system , pattern recognition (psychology) , mathematics , filter (signal processing) , image (mathematics) , filter design , psychology , pedagogy , geometry , combinatorics

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 region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system

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