Pen fouling is an undesired behaviour seen in growing pigs, where they start resting in the excretion area and excrete in the designated resting area. It is reasonable to assume that automatic monitoring of the location of the pigs within the pen could be used for early warnings of imminent pen fouling events. We intend to provide such automatic monitoring using convolutional neural networks (CNN) applied to images captured above the pens. In this preliminary study, we compared 12 different combinations of CNN architectures and training strategies for this purpose. The best performing strategy yielded an overall mean absolute error of 0.35 pigs and a coefficient of determination of 96% between the predicted and observed number of pigs in a given area of the pen.