Reliably predicting where people look in images and videos remains challenging and requires substantial eye-tracking data to be collected and analysed for various applications. In this paper, we present an eye-tracking study where twenty-eight participants viewed forty still scenes of video advertising. First, we analyse human attentional behaviour based on gaze data. Then, we evaluate to what extent a machine – saliency model – can predict human behaviour. Experimental results show that there is a significant gap between human and machine in visual saliency. The resulting eye-tracking data would benefit the development of saliency models for video advertising or other relevant applications. The eye-tracking data are made publicly available to the research community.