Efficient phase correlation motion estimation using approximate normalization

Motion estimation methods based on phase correlation have been in use for almost three decades. Its ability to measure large motions with subpixel accuracy has made it very appealing in many applications. Significant computational steps involved are FFT, normalized cross-power spectrum and IFFT. While efficient hardware and software solutions exist for FFT and IFFT, normalized cross-power spectrum computation has so far evaded efficient solutions. In this paper, we propose a novel method for computing cross-power spectrum based on approximate normalization of complex scalar, which is very efficient and hardware friendly as compared to existing methods. Proposed algorithm along with its generalization to normalization of complex vectors, results in significant reduction in precision requirements on FFT and IFFT as well. It is related to block floating point representation of numbers but overhead due to exponents have been avoided. Simulation results show that proposed algorithm holds potential for making phase correlation an accessible tool for low power devices as well as applications dealing with very large images.