The Horn-Schunck (HS) algorithm is one of the most popular optical flow estimation methods. Many scholars have proposed improved HS algorithms to improve accuracy. However

the efficiency of the HS algorithm remains an important problem because the HS algorithm requires much iterative computation. The HS algorithm is based on a differential method

and it only can compute small displacement optical flow. A multi-scale method has been proposed to solve the problem that a differential method cannot compute large displacement optical flow

but the efficiency of this method is slower than before. Fast methods are studied in this research to enhance efficiency. In the variation image restoration domain

fast methods for accelerating iteration have yielded good results

and some of the fast methods have been applied to small-displacement optical flow computation domain. In this study

Split Bregman method

dual method

and alternating direction method of multipliers are applied to large displacement optical flow computation for accelerating iteration. The accuracy

iteration

and time of different methods are compared quantitatively and qualitatively. The three fast methods all can obtain results with accuracy that is the same as that of the traditional method in lesser time. The time required for fast algorithms is 11%~42% of the time required for the traditional method. Computational efficiency can be improved greatly by applying these three fast methods to large displacement variation optical flow computing for different image sequences.