Motion Vector Estimation of Video Image by Pyramidal Implementation of Lucas Kanade Optical Flow

Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade Pyramidal implementation for both large & small motion In image pyramid representation a group of pixel information is gradually reduced to a value of one pixel information for both current & reference frame. The velocity & displacement at each pixel is obtained by using Lucas-Kanade equations. The original image is recovered by warping reference frame towards current frame using flow vectors i.e. velocity & displacement by using image warping techniques. The process is repeated until convergence. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences.