Eye movements are arguably the most natural and repetitive movement of a human being. The most mundane activity, such as watching television or reading a newspaper, involves this automatic activity which consists of shifting our gaze from one point to another. Identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present a number of problems. In this paper, we present the results of a new fixation identification technique that is based on clustering of eye positions, using projections and a projection aggregation applied to static pictures. We also present results of a new method that computes dispersion of eye fixations in videos considering a multi-user environment.

Eye movements are certainly the most natural and repetitive movement of a human being. The most mundane activity, such as watching television or reading a newspaper, involves this automatic activity which consists of shifting our gaze from one point to another. Identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification technique that is based on clustering of eye positions, using projections and projection aggregation applied to static pictures. We also present a new method that computes dispersion of eye fixations in videos considering a multiuser environment. To demonstrate the performance and usefulness of our approach we discuss our experimental work with two different applications: on fixed image and video.

Our demo focuses on eye tracking on web, image and video data. We use some state-of-the-art measurements, such as scan path, to determine how the user sees web documents, images and videos. Our approach is characterised by automatic eye/gaze tracking with non intrusive sensors, mainly infrared cameras of web, image and video documents. We analyse eye/gaze tracking concerns spatial regions of static documents (images and web pages) and spatial zones of dynamic documents (video, sequence of web pages hyperlinked). In the context of dynamic documents, the eye/gaze tracking is processed image-per -image in video documents, and page-per-page in web documents. The result is more rough on video, and more accurate on images and hyperlinked web pages. Eye/gaze tracking on video is relatively new and unexplored in the literature.