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Template Matching for Target Tracking from the Bayesian Probability Fusion of Two Different Matching Algorithms

Zhen Jia, Arjuna Balasuriya, Subhash Challa

Abstract


In this paper a Bayesian probability hybrid based template matching method is proposed for
target tracking. Two different template matching methods are weighted by their matching probabilities and then combined through the Bayesian theory to give a f inal robust template updating and matching. Here the matching probability for each method is assigned with a Gaussian Probability Distribution Function (PDF). Then the template’s best matched region in the image is estimated with the maximum likelihood algorithm from the joint distribution of these two template matching PDFs. In this paper the f irst method is the commonly used Sum of the Squared Errors (SSE) template matching method. The second one is the Gaussian Mixture Models (GMMs) method, which is used to represent the template’s appearance features. With the fusion of these two template matching methods, the algorithm in this paper can well deal with the problems in template matching such as template drifting, shape rotation, appearance changes, occluded object tracking or environmental lighting condition changes.

Keywords


Target Tracking, Template Matching and Updating, Sum of Squared Errors (SSE), Gaussian Mixture Models (GMMs), Gradient Descent, Expectation-Maximization (EM), Bayesian, RGB Color, Maximum Likelihood (ML), Probability Distribution Function (PDF), Fusion.

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