A Comparative Study on Gradient-Based Approaches for Optical Flow Estimation

The most readily available motion parameter from sequential image is optical flow. Among various optical flow estimation techniques, gradient-based approach is a common technique. This approach is based on the assumption that the brightness of a point in the image remains constant during a short time interval, while the location of that point in the image may change due to motion. This assumption leads to a single local constraint on the optical flow at a certain point in the image. It is, however, ill-posed as the constraint constitutes only one equation of two unknowns, that is, xcomponent and y-component of the flow vector. In order to solve this problem, various methods have been proposed. There are, however, only a few comparative studies from the viewpoint of the application to the specific and practical motion analysis. This paper reviews the gradient-based approaches theoretically and compares their performance empirically from the point of view of application to vehicle motion analysis. The basic methods of gradient-based approach are reviewed as follows: (1) Increase in the number of observation equations: (a) spatial local optimization method, (b) temporal local optimization method, (c) multispectral constraints method, (d) second order derivative method and by their combination; (2) Imposition of a condition: (a) spatial global optimization method, (b) temporal global optimization method and their combination. The result of empirical comparison shows the difficulty of estimation of precise and dense optical flow by ordinary gradient-based approaches, when sequential images are taken at an interval about 1/30 seconds. Hence, it is difficult to analyze vehicle motion by the gradient-based approaches in this case.