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.
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. Read More
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