Due to the huge repercussion of football broadcast in society, an enormous number
of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about
the content of the images. In particular, the type of view in a football image contains
valuable information about the game. Thus, the type of view must be automatically
computed to be able to process the large amount of information extracted from each
In this work, we propose a robust classification system that estimates the type of
view in football images in real time. For each frame of the sequence, a set of descriptors is extracted to characterize a specific part of the scene: the grass field.
Gathering all these descriptors and a few ones related with texture, a decision tree
determines the view that is shown in that frame. In order to improve the robustness
of the algorithm, the redundancy of the temporal domain is exploited.
The validity of the proposed algorithm has been tested on a large amount of frames
from broadcasted football sequences in a wide variety of scenarios (stadiums, light
conditions, ...). Promising results have been obtained with a 96% of accuracy in the
classification of these images.
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