The registration of images of soccer matches is a key stage in many computer vision applications. Until now, this task has been typically carried out from key points obtained from the white line marks drawn on the field of play, but in many cases this does not yield enough keypoints for a robust registration. This article proposes a strategy to detect the borders between the grass bands of the field of play and therefore makes it possible to locate many more key points that will allow to carry out a subsequent registration of the images.

First, a preprocessing is applied to obtain a grayscale image in which the grass bands are easily distinguishable, and also to obtain a binary mask of the entire field of play that determines the area of interest. Then, a local analysis is carried out to detect most of the borders between grass bands. Finally, a global analysis based on the intersections between lines is applied to group the detected borders and rule out false detections.

The strategy has been evaluated on two databases composed of hundreds of annotated images from matches in several stadiums with different characteristics and light conditions. The results obtained have shown that most of the lines delimiting the grass bands are found successfully, while the number of false detections is very small.

足球比赛图像的注册是许多计算机视觉应用中的关键阶段。到目前为止，这项任务通常是根据从比赛场地上绘制的白线标记获得的关键点执行的，但在许多情况下，这并不能产生足够的关键点来进行稳健的配准。本文提出了一种检测球场草带之间边界的策略，因此可以定位更多的关键点，以便进行图像的后续配准。

首先，应用预处理以获得草带易于区分的灰度图像，并获得确定感兴趣区域的整个运动场的二进制掩码。然后，进行局部分析以检测草带之间的大部分边界。最后，基于线之间的交叉点的全局分析被应用于对检测到的边界进行分组并排除错误检测。

该策略已在两个数据库上进行了评估，该数据库由数百个来自具有不同特征和光照条件的体育场的比赛的注释图像组成。得到的结果表明，大部分草带的划界线都被成功找到，而错误检测的数量非常少。