Kyoto University

ACM, (2012). This is the authors version of the work. It is posted here by permission of ACM for your personal use. Not for redis-tribution. The definitive version was published in the proceeding of Eye Tracking Research and Applications (ETRA 2012). Videos used in this research including Figures 1, 2, 3 and 5 were provided by courtesy of Panasonic Corporation. We present a method to analyze a relationship between eye move-ments and saliency dynamics in videos for estimating attentive states of users while they watch the videos. The multi-mode saliency-dynamics model (MMSDM) is introduced to segment spatio-temporal patterns of the saliency dynamics into multiple sequences of primitive modes underlying the saliency patterns. The MMSDM enables us to describe the relationship by the local saliency dynamics around gaze points, which is modeled by a set of distances between gaze points and salient regions characterized by the extracted modes. Experimental results show the effectiveness of the proposed model to classify the attentive states of users by learning the statistical difference of the local saliency dynamics on gaze-paths at each level of attentiveness