智能交通系统中，车辆的行驶速度是重要的参数之一，为了快速获得车辆的行驶速度，提出了一种基于位移场的运动场估计计算法，该方法首先构造一个基于图象灰度的指标函数，再通过变分法中的欧拉方程分析和偏微分方程的数值解法，即可得到序列图象间的位移场；然后利用帧间位移场、帧间时间差、摄像机与车辆间的距离等参数相结合可计算出车辆的行驶速度，实验结果表明，与块匹配法相比，该方法不仅可得到更好的位移场，而且可快速获得车辆的行驶速度。

The vehicle speed is one of the most important parameters in intelligent transportation system. In this paper, a new algorithm for displacement field estimation is present to get vehicle speed. This method can detect the big displacement vectors between frames. In order to alleviate the over smooth problem between the motion object and non motion object, a discontinuity preserving function, Huber function, is introduced instead of globe smoothness constraint term. We first create an energy function, we can get based on the image gray. The energy function consists of two terms:one is matching error;the other is a smoothness constraint. Minimizing this energy function, we can get the displacement field between frames. Using Euler's equation and partial derivative equation, the displacement field could be calculated. The displacements of corners are computed firstly,and then the displacement vectors are smoothed along the tangential direction of edge. The vehicle speed could be getting through calculating displacement field, time and the distance between camera and vehicle, Experiment shows, using this method result is good.