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This correspondence proposes a novel template matching technique using a fourth central moment. The fourth central moment is an established estimator which uses higher order statistics theory, important in the presence of an additive Gaussian noise. By use of some substitutions and complex arithmetic, computation of the fourth central moment is derived from correlation functions of substituting functions. The fourth central moment can be computed using the fast Fourier transform (FFT) approach. Simulation results show that the proposed algorithm performs better than the classical estimators in terms of robustness, while the extra computational cost is negligible.

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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