Frequency domain techniques for motion estimation

Motion estimation is defined as searching the best motion vector, which is the displacement of the coordinate of the best similar block in previous frame for the block in current frame. It has been extensively studied by both computer vision and human vision researchers for more than 30 years. The output of motion estimation is used in various areas such as video compression, segmentation, pattern tracking and camera motion stabilization.Motion estimation techniques could be classified into four main categories: techniques based on spatio-temporal differentials, techniques based on matching, Fourier techniques and techniques using feature extraction.In this thesis, four frequency domain techniques which are phase correlation, gradient correlation, statistically robust correlation, and robust correlation were examined. To be able to detect local motion, image was divided into small blocks and the blocks that are in the same location on the consecutive frames were converted into frequency domain. Then, one of the correlation methods (phase correlation, cross correlation, etc?) were applied in the frequency domain. After inverse Fourier transform, one peak and five biggest peaks were being searched in the correlation function and their performance comparison were made using peak signal to noise ratio (PSNR) of displacement field difference as a measure of success.