为了准确及时获取船舶在船闸中的运动信息，提出了一种基于概率速度场的实时检测船闸中船舶的方法。该方法首先分析了船舶在船闸中运行的各种环境因素；其次为了消除和减弱阴影和光斑的影响，从监测图象灰度直方图中派生出一个高斯概率分布模型，进而获取图象的概率场；最后定义了一个基于光流计算的可实时检测船舶的概率速度场。该方法已成功地测试了大量通过三峡临时船闸和葛洲坝船闸的船舶序列图象。

In the paper, an on|line ship detecting system based on the probability velocity field is presented. First all environment factors that ships go through the ship lock are analyzed, such as lighting, shading, waving and so on; Second, a Gaussian probability distribution model(probability field) is generated from gray|level histogram to diminish the affection of shades and speckles, since shades and speckles' gray|levels are distributed in both sides of gray|level histogram, after histogram is fitted with the Gaussian distribution function, the probability of them is smaller than that of other gray|levels. Therefore a probability velocity field is defined and derived based on the optical flow to detect ships in lock on|line. The velocity field is calculated by the traditional optical flow estimation method using the probability of gray|levels, not the gray|levels. Finally the affection of shading and lighting is reduced greatly. The method has succeeded in testing a larger number of image sequences that ships pass through the temporary ship lock of the three gorges project(TGP) and ship lock of the Gezhou dam.