Motion detection using Lucas Kanade algorithm and application enhancement

Abstract

Currently, computational of the optical flow of a sequence of images still remains a challenge in video processing. There are no specific techniques that can sufficiently generate an accurate and dense optical flow. Computational using local variable such as Lucas Kanade algorithm does not provide a good segmentation which indirectly affects the pattern of the optical flow obtained. In this paper, we will only focus on differential methods which are Lucas Kanade and Horn Schunck. We investigated the difference in standalone Lucas Kanade algorithm and the effect when it is combined with global variable such as number of iteration and smoothing from Horn Schunck algorithm and filtering. Comparison is made based on the optical flow pattern, segmentation of the motion of the images and the processing time. Experiments on the images show that by using the derivation of partial derivative in Lucas Kanade in Horn Schunck algorithm with the smoothing effect and number of iteration along with filters will result in better segmentation and better optical flow. Thus, this shows that the computation of intensity will influence the optical flow.