Using gradient correlation for sub-pixel motion estimation of video sequences

A highly accurate and computationally efficient method is presented suitable for the estimation of motion in video sequences. The method is based on the maximization of the spatial gradient cross-correlation function, which is computed in the frequency domain and therefore can be implemented by fast transformation algorithms. We present enhancements to the baseline gradient-correlation algorithm, which further improve performance, especially in the presence of manually induced additive Gaussian noise. We also present a comparative performance analysis, which demonstrates that the proposed method outperforms the state-of-the-art in frequency-domain motion estimation, in the shape of phase correlation.